--Image reconstruction from undersampled k - space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and has also shown potential to significantly speed up MRI reconstruction with reduced measurements. This article gives an overview of deep learning -based image reconstruction methods for MRI. Three types of deep learning -based approaches are reviewed, the data - driven, model - driven and integrated approaches. T he main structure of each network in three approaches is explained and the analysis of common parts of reviewed networks and differences in - between are highlighted. Based on the review, a number of signal processing issues are discussed for maximizing the potential of deep reconstruction for fast MRI. The discussion may facilitate further development of "optimal" network and performance analysis from a theoretical point of view. I. INTRODUCTION Since its inception in the early 70's, magnetic resonance imaging (MRI) has revolutionized radiology and medicine. However, MRI is known to be a slow imaging modality and many techniques have been devel oped to reconstruct the desired image from undersampled measured data to improve the imaging speed [1]. During the past decades, compressed sensing (CS) has become an important strategy for fast MR imaging based on the sparsity prior. However, the iterative solution procedure takes a relatively long time to achieve a high -quality reconstruction, and the selection of the regularization parameter is empirical.

Lightweight tagging expands information and activity management practices.

In this era of technological advancements, the success of every business, organization or startup prominently lies with its capability to search through and process data effectively. Significantly, in business terminologies, it is mostly perceived that the said task is quite prominent and caters the needs of business firms to promote versatility and significance via amalgamation with new and cutting-edge technologies such as Artificial Intelligence, Machine Learning, Image Processing and Data Mining. Synonymously, Data Mining is an important aspect meant to provide relevant information that empowers you to understand the future trends, and should also be a key element to drive your business to the next level. Since digital marketing trends are flourishing and could hardly work without providing the essential data which further transforms into information which is further transited in the form of knowledge. Moreover, this allows for devising new capabilities and applications of sales strategy, thus it shows how data mining performs its functionalities.

In a decentralised knowledge representation system such as the W eb of Data, it is common and indeed desirable for different knowledge graphs to overlap. Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Whilst the deductive value of such identity statements can be extremely useful in enhancing various knowledge-based systems, incorrect use of identity can have wide-ranging effects in a global knowledge space like the W eb of Data. With several works already proven that identity in the W eb is broken, this survey investigates the current state of this "sameAs problem". An open discussion highlights the main weaknesses suffered by solutions in the literature, and draws open challenges to be faced in the future.

Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they often turn into evolving environments where a change may affect the input data distribution. Such a change causes that predictive models trained over these stream data become obsolete and do not adapt suitably to new distributions. Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores. Unfortunately, most off-the-shelf classification models need to be retrained if they are used in changing environments, and fail to scale properly. Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks. Besides, some specific flavors of Spiking Neural Networks can overcome the necessity of retraining after a drift occurs. This work intends to merge both fields by serving as a comprehensive overview, motivating further developments that embrace Spiking Neural Networks for online learning scenarios, and being a friendly entry point for non-experts.

The UK is planning to harness AI to help determine the condition of roads and where investment should be prioritised. British drivers are well-accustomed to poor road conditions, especially potholes and the long delays in getting them fixed (one ingenious man has even come up with an innovative way of getting the council to fix them faster...) To be fair to councils, keeping all the roads in top condition is expensive. Factors like minimising disruption along busy routes, and planning diversions, must also be considered. Fortunately, AI is beginning to help automate this automotive dilemma. The Department for Transport (DfT) has awarded £2m in funding to a project using AI to examine the condition of roads, forming part of a wider £350 million funding package.

Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating on two real-world datasets the most recent trends in electric load forecasting, by contrasting deep learning architectures on short term forecast (one day ahead prediction). Specifically, we focus on feedforward and recurrent neural networks, sequence to sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one.

Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years. In this article, we provide an overview over these recent advancements, focusing on neural network based question answering systems over knowledge graphs. We introduce readers to the challenges in the tasks, current paradigms of approaches, discuss notable advancements, and outline the emerging trends in the field. Through this article, we aim to provide newcomers to the field with a suitable entry point, and ease their process of making informed decisions while creating their own QA system.

Recent research has helped to cultivate growing awareness that machine learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination. We also survey how discrimination is commonly measured, and suggest how familiar development processes can be augmented to mitigate systems' discriminatory potential. We advocate that data scientists should be intentional about modeling and reducing discriminatory outcomes. Without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity.

The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, which collect and share information to reflect the status of physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of time. The integration of IoT and ACS results in a new concept - autonomous IoT (AIoT). The sensors collect information on the system status, based on which intelligent agents in IoT devices as well as Edge/Fog/Cloud servers make control decisions for the actuators to react. In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for decision making. In this paper, we first provide comprehensive survey of the state-of-art research, and then propose a general model for the applications of RL/DRL in AIoT. Finally, the challenges and open issues for future research are identified.