A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks. There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method, which we call smart augmentation and we show how to use it to increase the accuracy and reduce over fitting on a target network. Smart augmentation works, by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart augmentation has shown the potential to increase accuracy by demonstrably significant measures on all data sets tested. In addition, it has shown potential to achieve similar or improved performance levels with significantly smaller network sizes in a number of tested cases.

In this paper, a review is presented for the research on eye gaze estimation techniques and applications, which has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive, and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for the performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.

In the last few years, we have witnessed an exponential growth in research activity into the advanced training of convolutional neural networks (CNNs), a field that has become known as deep learning. This has been triggered by a combination of the availability of significantly larger data sets, thanks in part to a corresponding growth in big data, and the arrival of new graphics-processing-unit (GPU)-based hardware that enables these large data sets to be processed in reasonable timescales. Suddenly, a wide variety of long-standing problems in machine learning, artificial intelligence, and computer vision have seen significant improvements, often sufficient to break through long-standing performance barriers. Across multiple fields, these achievements have inspired the development of improved tools and methodologies leading to even broader applicability of deep learning. The new generation of smart assistants, such as Alexa, Hello Google, and others, have their roots and learning algorithms tied to deep learning. In this article, we review the current state of deep learning, explain what it is, why it has managed to improve on the long-standing techniques of conventional neural networks, and, most importantly, how you can get started with adopting deep learning into your own research activities to solve both new and old problems and build better, smarter consumer devices and services.

Current activities in the Internet of Things (IoT) are focused on architectures, protocols, and networking for the efficient interconnection of heterogeneous things, infrastructure deployment, and creation of value-added services. The majority of the IoT products, services, and platforms are supported by cloud-computing platforms. With the IoT being a multidisciplinary ecosystem, it is now being utilized in connection with scenarios demanding real-time data processing and feedback, for example, connected and autonomous vehicles scenarios. Cloud platforms are not suitable for scenarios involving real-time operation, low latency requirements, and high quality of service (QoS). Recently, mobile-edge computing (MEC) has gained momentum from the industry to address the mentioned requirements. MEC is a novel paradigm that extends cloud-computing capabilities and services to the edge of the network. Due to dense geographical distribution, proximity to consumers, support for high mobility, and open platform, MEC can support applications and services with reduced latency and improved QoS. Thus, MEC is becoming an important enabler of consumer-centric IoT applications and services that demand real-time operations. The OpenFog Consortium and standards development organizations like ETSI have also recognized the benefits the IoT and MEC can bring to consumers. Potential applications for MEC-enabled IoT include smart mobility, connected vehicles, emergency response, smart cities, content distribution, and location-based services.

Blockchain technology is ready to disrupt nearly every industry and business model, and the energy sector is no exception. Energy businesses across the world have already started exploring the use of blockchain technology in large-scale energy trading systems, peer-to-peer energy trading, project financing, supply chain tracking, and asset management among other applications. Information and Communication Technologies (ICTs) recently started revolutionizing the energy landscape, and now blockchain technology is providing an additional opportunity to make the energy system more intelligent, efficient, transparent, and secure in the longer term. The idea of this paper is to examine more closely the use of blockchain technology for its possible application in the energy efficiency industry and to determine how it could make energy efficiency markets more secure and transparent in the longer term. This paper examines in detail the key benefits and implications of using blockchain in the energy efficiency sector through the presentation and discussion of two case studies as possible blockchain applications—(i) the UK Energy Company Obligation scheme and (ii) the Italian White Certificate Scheme. We have presented how the key issues around trading energy efficiency savings—correctly estimating the savings, data transparency among stakeholders, and inefficient administrative processes—can be solved through the application of a blockchain-based smart contract system. Finally, this paper presents an implementation of a smart contract for trading of energy-saving certificates achieved via execution of smart contract transactions on the Ethereum blockchain.

Real time face detection combined with eyegaze tracking can provide a means of user input into a gaming environment. Game and CE system designers can use facial and eye-gaze information in various ways to enhance UI design providing smarter modes of gameplay interaction and UI modalities that are sensitive to a users behaviors and mood. Here we review earlier approaches, using wearable sensors, or enhanced IR illumination. Our technique only requires video feed from a low-resolution user-facing camera. The algorithm is described and some comparative results on a range of embedded hardware are provided. The potential for using eye-gaze as a means of direct user input and improving the accuracy of estimation accordingly is also discussed.