Joint Transaction Transmission and Channel Selection in Cognitive Radio Based Blockchain Networks: A Deep Reinforcement Learning Approach

To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed

Overcoming challenges in the classification of deep geothermal potential

The geothermal community lacks a universal definition of deep geothermal systems. A minimum depth of 400 m is often assumed, with a further sub-classification into middle-deep geothermal systems for reservoirs found between 400 and 1000 m. Yet, the simplistic use of a depth cut-off is insufficient to uniquely determine the type of resource and its associated potential. Different definitions and criteria have been proposed in the past to frame deep geothermal systems. However, although they have valid assumptions, these frameworks lack systematic integration of correlated factors. To further complicate matters, new definitions such as hot dry rock (HDR), enhanced or engineered geothermal systems (EGSs) or deep heat mining have been introduced over the years. A clear and transparent approach is needed to estimate the potential of deep geothermal systems and be capable of distinguishing between resources of a different nature. In order to overcome the ambiguity associated with some past definitions such as EGS, this paper proposes the return to a more rigorous petrothermal versus hydrothermal classification. This would be superimposed with numerical criteria for the following: depth and temperature; predominance of conduction, convection or advection; formation type; rock properties; heat source type; requirement for formation stimulation and corresponding efficiency; requirement to provide the carrier fluid; well productivity (or injectivity); production (or circulation) flow rate; and heat recharge mode. Using the results from data mining of past and present deep geothermal projects worldwide, a classification of the same, according to the aforementioned criteria is proposed

Advanced analytics as a tool to identify ways to achieve sustainable development

At this stage of information society is a rational mechanism for the achievement of sustainable development through the use of management information systems. Advanced Analytic System allows "deep" data mining, forecasting and optimization decision making. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3180

Mid-level Deep Pattern Mining

Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). Specifically, we find that for an image patch, activations extracted from the first fully-connected layer of CNNs have two appealing properties which enable its seamless integration with pattern mining. Patterns are then discovered from a large number of CNN activations of image patches through the well-known association rule mining. When we retrieve and visualize image patches with the same pattern, surprisingly, they are not only visually similar but also semantically consistent. We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used. Our approach also outperforms or matches recent works using CNN for these tasks. Source code of the complete system is available online.Comment: Published in Proc. IEEE Conf. Computer Vision and Pattern Recognition 201

Deep Boosting: Layered Feature Mining for General Image Classification

Constructing effective representations is a critical but challenging problem in multimedia understanding. The traditional handcraft features often rely on domain knowledge, limiting the performances of exiting methods. This paper discusses a novel computational architecture for general image feature mining, which assembles the primitive filters (i.e. Gabor wavelets) into compositional features in a layer-wise manner. In each layer, we produce a number of base classifiers (i.e. regression stumps) associated with the generated features, and discover informative compositions by using the boosting algorithm. The output compositional features of each layer are treated as the base components to build up the next layer. Our framework is able to generate expressive image representations while inducing very discriminate functions for image classification. The experiments are conducted on several public datasets, and we demonstrate superior performances over state-of-the-art approaches.Comment: 6 pages, 4 figures, ICME 201