Reviewerly/exHarmony huggingface dataset:
EC(DH)&lt;SUP align="right"&gt;2&lt;/SUP&gt;: an effective secured data storage mechanism for cloud based IoT applications using elliptic curve and Diffie-Hellman

In the rapid growth of cloud computing with internet of things (IoT) technology utilisation in this fast world, security is an essential issue in cloud data storage and access the encrypted data. In the past, several researchers have worked on data storage mechanisms and came up with different ideas to fulfil the cloud user's expectation. Even though, they have failed to provide the sufficient security on cloud. For providing better security over the cloud, we propose a new elliptic curve and Diffie-Hellman based data storage mechanism called EC(DH)2. Here, the standard Diffie-Hellman algorithm is used twice as DH2 and it is combined with the standard elliptic curve cryptographic algorithm for performing secured storage in cloud based IoT applications. The experiments have been conducted by using the various sizes of documents and files for evaluating the proposed EC(DH)2 algorithm and is proven that it provides better protection and increased key size.
Tracking via context-aware regression correlation filter with a spatial–temporal regularization

Correlation filters are known to have superior performance in tracking speed. To improve tracking results, we propose a context-aware regression correlation filter with a spatial–temporal regularization for tracking. First, the spatial regularization parameters are computed by the spatial correlation between the target and the surrounding information. Meanwhile, the filter weight distribution is also computed to highlight the target region and suppress the background region. Next, a context-aware model is proposed to adaptively expand the search area of the target with the original regression analysis. Due to the introduction of the context-aware information, the target structure will be changed and the regression model will not be adapted to the Gaussian function. Therefore, an optimized regression objective function is constructed according to the context-aware model. In the tracking process, under rotation, out-of-view, deformation, etc., the tracker still can continue the tracking of subsequent frames according to partial background information. In addition, time variables for online updating of the spatial–temporal model employ the similarity of adjacent frames to achieve more accurate tracking. According to the model form, the alternative direction method of multipliers is used to optimize the model optimization. Extensive experimental results on the OTB-2013 and OTB-2015 dataset object tracking benchmarks prove that the proposed algorithm is superior to other state-of-the-art algorithms in terms of success, accuracy, and robustness.
End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-Net.

Information extraction from document images has received a lot of attention recently, due to the need for digitizing a large volume of unstructured documents such as invoices, receipts, bank transfers, etc. In this paper, we propose a novel deep learning architecture for end-to-end information extraction on the 2D character-grid embedding of the document, namely the \textit{Multi-Stage Attentional U-Net}. To effectively capture the textual and spatial relations between 2D elements, our model leverages a specialized multi-stage encoder-decoders design, in conjunction with efficient uses of the self-attention mechanism and the box convolution. Experimental results on different datasets show that our model outperforms the baseline U-Net architecture by a large margin while using 40\% fewer parameters. Moreover, it also significantly improved the baseline in erroneous OCR and limited training data scenario, thus becomes practical for real-world applications.
A Quantum Router Architecture for High-Fidelity Entanglement Flows in Multi-User Quantum Networks

The distribution of quantum entanglement between remote clients offers opportunities for secure communication, distributed quantum computation, and quantum-enhanced sensing. A key technology for allowing such a quantum network to extend over large distances is the quantum repeater, which typically employs a long-lived quantum memory with an optical interface. The past decade has seen tremendous progress in experimentally realizing the fundamental building blocks of these repeaters, but open questions remain on what architectures achieve optimal entanglement flows in multi-user quantum networks. Here, we propose a quantum router architecture comprising many quantum repeater nodes in a flexible photonic switchboard architecture to broker entanglement flows across such networks. We compute the rate and fidelity of entanglement distribution using this architecture using an event-based simulation platform. We find that the router's photonic switchboard improves the entanglement fidelity with the number of qubits per node, without a significant drop in the entanglement distribution rate. The proposed quantum router architecture uses present-day photonic switch technology such as photonic integrated circuits, opening a path to near-term deployable multi-user quantum networks.
Learning Based Spectrum Hole Detection for Cognitive Radio Communication

This paper proposes a novel learning based (LB) solution for detection and quantification of spectrum holes in periodic communications of unmanned aerial vehicles (UAVs).Instead of hypothesis testing after implementation of spectrum sensing methods, the implemented LB solution based on spectral correlation function (SCF) uses region convolutional neural network (R-CNN) for extracting quantitative parameters of the spectrum holes.The proposed LB approach is implemented using GoogLeNet architecture for the wide band detection in the scenario of orthogonal frequency division multiplexing (OFDM) communication system with the additive white Gaussian noise (AWGN) channel model.The simulation of single input single output (SISO) communication system with spectrum holes is presented.Examples of wide band detection results for both SISO and multiple input multiple output (MIMO) systems are shown and the proposed LB detector is found to be fairly accurate in identification of spectrum holes.By analyzing the training performance, the GoogLeNet architecture, along with its hyperparameter configurations and training dataset is validated.We also demonstrated that our LB detector is resilient to the AWGN environment by analyzing the precision and recall curves, average precision and mean relative error (MRE) versus signal noise ratio (SNR).
A local model of quantum Turing machines

The model of local Turing machines is introduced, including classical and quantum ones, in the framework of matrix-product states. The locality refers to the fact that at any instance of the computation the heads of a Turing machine have definite locations. The local Turing machines are shown to be equivalent to the corresponding circuit models and standard models of Turing machines by simulation methods. This work reveals the fundamental connection between tensor-network states and information processing.
Correlation Matters: Multi-scale Fine-Grained Contextual Information Extraction for Hepatic Tumor Segmentation

Automatic tumor segmentation has been used as a diagnostic aid in the identification of diseases such as tumors from liver CT scans, and their treatment. Owing to their success in computer vision tasks, the state-of-the-art Fully Convolutional Networks (FCNs) or U-Net based models have often been employed in many recent studies for automatic tumor segmentation to learn numerous weight-shared convolutional kernels and extract various semantic features. However, the correlation between different tumor regions in feature maps cannot be easily captured due to the lack of contextual dependencies, which in turn limits the representative capability of the adopted models and thus affects the accuracy of tumor segmentation results. To resolve this issue, we propose a novel framework for segmentation of tumors in liver CT scans, which can explicitly extract multi-scale fine-grained contextual information by adaptively aggregating local features with their global dependencies. The proposed multi-scale framework features a light model with a very few additional parameters, and also its visualization capability significantly boosts networks’ interpretability. Experimental results on a real-world liver tumor CT dataset illustrate that the proposed framework achieves the state-of-the-art performance in terms of a number of widely used evaluation criteria for the hepatic tumor segmentation task.
Extractive Single Document Summarization Using NSGA-II

Text summarization is the process of automatically retrieving the most relevant information from a large amount of textual data and representing the retrieved information in a coherent way. In this paper, extractive single document summarization is formulated as a multi-objective optimization problem with two objectives—one to maximize the information extracted and the other to minimize the summary length. The non-dominated solution that conveys the main theme of the document is summarized with minimum words using non-dominated sorting genetic algorithm-II (NSGA-II). The unique characteristic of the proposed NSGA-II-based summarizer is that the feature scores are computed on both unigram and bigram of the sentence. The proposed NSGA-II-based summarizer is trained on DUC 2001 and evaluated on DUC 2002 benchmark datasets and shows significant improvement in ROUGE score compared to other state-of-the-art methods.
Excellence in Drawing up Marketing Mix Strategies for Small and Medium Enterprises (SMEs) and Their Impact on the Marketing Performance

This research aims at identifying the impact of excellence in drawing up the following four marketing mix strategies (Product, Pricing, Promotion and Distribution) of the small and medium enterprises in Jordan, in terms of their marketing performance in its dimensions (Sales Growth, Profit Growth, Customer Attraction and Customer Retention).In order to reach the results of this study, A total of (187) valid questionnaire surveys were collected from companies belong to the SME Association in Jordan. The Statistical Package for the Social Sciences (SPSS) approach was used to analyze the collected data. The empirical results indicated there is a significant relationship between the building of marketing strategies of the marketing mix elements in the Jordanian SME and their marketing performance, by (sales growth, profit growth, customer attraction, and customer retention) dimensions. Consequently, decision makers in small and medium organizations need to choose strategies based on their target market to the positive impact on the mind of the consumer, which in turn could improve modern scientific methods in SME to divide their markets into sub-market sectors.
Top-down attention recurrent VLAD encoding for action recognition in videos

Most recent approaches for action recognition from video leverage deep architectures to encode the video clip into a fixed length representation vector that is then used for classification. For this to be successful, the network must be capable of suppressing irrelevant scene background and extract the representation from the most discriminative part of the video. Our contribution builds on the observation that spatio-temporal patterns characterizing actions in videos are highly correlated with objects and their location in the video. We propose Top-down Attention Recurrent VLAD Encoder (TA-VLAD), a deep recurrent neural architecture with built-in spatial attention that performs temporally aggregated VLAD encoding for action recognition from videos. We adopt a top-down approach of attention, by using class specific activation maps obtained from a deep Convolutional Neural Network pre-trained for generic image recognition, to weight appearance features before encoding them into a fixed-length video descriptor with a Gated Recurrent Unit. Our method achieves state-of-the-art recognition accuracy on HMDB51 and UCF101 benchmarks.
Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection.

Recent researches attempt to improve the detection performance by adopting the idea of cascade for single-stage detectors. In this paper, we analyze and discover that inconsistency is the major factor limiting the performance. The refined anchors are associated with the feature extracted from the previous location and the classifier is confused by misaligned classification and localization. Further, we point out two main designing rules for the cascade manner: improving consistency between classification confidence and localization performance, and maintaining feature consistency between different stages. A multistage object detector named Cas-RetinaNet, is then proposed for reducing the misalignments. It consists of sequential stages trained with increasing IoU thresholds for improving the correlation, and a novel Feature Consistency Module for mitigating the feature inconsistency. Experiments show that our proposed Cas-RetinaNet achieves stable performance gains across different models and input scales. Specifically, our method improves RetinaNet from 39.1 AP to 41.1 AP on the challenging MS COCO dataset without any bells or whistles.
Design and Optimization of a Deep Neural Network Architecture for Traffic Light Detection

Autonomous Driving has recently become a research trend and efficient autonomous driving system is difficult to achieve due to safety concerns, Applying traffic light recognition to autonomous driving system is one of the factors to prevent accidents that occur as a result of traffic light violation. To realize safe autonomous driving system, we propose in this work a design and optimization of a traffic light detection system based on deep neural network. We designed a lightweight convolution neural network with parameters less than 10000 and implemented in software. We achieved 98.3% inference accuracy with 2.5 fps response time. Also we optimized the input image pixel values with normalization and optimized convolution layer with pipeline on FPGA with 5% resource consumption.
Sequential Models for Text Classification Using Recurrent Neural Network

Neural network-based applications are recently shown promising results for text classification.However, it is still challenging for the model to contemplate local features and word contingent on the information of the sentence.This work proposed a deep learning approach to generate a more precise sentence that leverages the preceding texts when classifying a subsequent one.One of the deep learning methods used is Recurrent Neural Network (RNN) with the architecture Long Short-Term Memory (LSTM).By training four variant models of 1-layer LSTM for each balance dataset in pre-processing process with 20,000, 25,000, 30,000, 35,000, 40,000, and 45,000 using optimizer Adam and RMSProp.The results show that; first, the more data input, the higher the accuracy it gets and the second is Adam can perform better as optimizer than RMSProp in this research.The highest Precision, Recall, and F1-score obtain are 97.
Approach to assessing the maturity level of an educational institution of secondary vocational education

Abstract This article discusses the approach to determining the maturity level of the organization of secondary vocational education on the example of the Krasnoyarsk College of Radio Electronics and Information Technology. The study is based on a survey of teachers and college staff for different periods of time. The research structure is based on the Voyager Plant Optimization methodology, the questionnaires are designed in accordance with the McKinsey model and adapted to the system of secondary vocational education in Russia. The survey was conducted in two directions: personnel management and increasing efficiency and showed the degradation of the organization’s maturity level under the influence of environmental factors of the organization. The results reflect a prolonged transition process in the college caused by a continuous sequence of organizational changes, such as: transfer to the Krasnoyarsk Territory in 2012, merger with the Krasnoyarsk College of Radio Electronics, Economics and Management in 2015, a constant change of leadership from 2015 to 2019.
Creative Quantum Computing: Inverse FFT Sound Synthesis, Adaptive Sequencing and Musical Composition

Quantum computing is emerging as an alternative computing technology, which is built on the principles of subatomic physics. In spite of continuing progress in developing increasingly more sophisticated hardware and software, access to quantum computing still requires specialist expertise that is largely confined to research laboratories. Moreover, the target applications for these developments remain primarily scientific. This chapter introduces research aimed at improving this scenario. Our research is aimed at extending the range of applications of quantum computing towards the arts and creative applications, music being our point of departure. This chapter reports on initial outcomes, whereby quantum information processing controls an inverse Fast Fourier Transform (FFT) sound synthesizer and an adaptive musical sequencer. A composition called Zeno is presented to illustrate a practical real-world application.
Finanzwirtschaftliche Anwendungen der Blockchain-Technologie

The blockchain technology was first implemented in 2009 as the basis of the cryptocurrency Bitcoin. The technology is said to be a disruptive technology that has the potential to significantly affect many areas of the economy. In this paper we provide a survey of the blockchain technology and its applications in finance. We focus on cryptocurrencies, smart contracts, initial coin offerings, the clearing and settlement of transactions in financial markets, and implications for the governance of exchange-listed firms.
Intralingual translation and cascading crises

During crises, intralingual translation (or simplification) of medical content can facilitatecomprehension among lay readers and foster their compliance with instructions aimed toavoid or mitigate the cascading effects of crises. The onus of simplifying health-relatedtexts often falls on medical experts, and the task of intralingual translation tends to be nonautomated. Medical authors are asked to check and remember different sets of plainlanguage guidelines, while also relying on their interpretation of how and when toimplement these guidelines. Accordingly, even simplified health-related texts presentcharacteristics that make them difficult to read and comprehend, particularly for anaudience with low (health) literacy. Against this background, this chapter describes anexperimental study aimed at testing the impact that using a controlled language (CL)checker to semi-automate intralingual translation has on the readability andcomprehensibility of medical content. The study focused on the plain language summariesand abstracts produced by the non-profit organisation Cochrane. Using Coh-Metrix andrecall, this investigation found that the introduction of a CL checker influenced somereadability features, but not lay readers’ comprehension, regardless of their nativelanguage. Finally, strategies to enhance the comprehensibility of health content and reducethe vulnerability of readers in crises are discussed.
The research of process of applying mobile ICT by university students: mobile testing systems and mobile means of multimedia development

The study is aimed at theoretical substantiation, development and experimental testing of methods of applying mobile technologies by university students. The objectives of the study imply adapting mobile testing systems and mobile means of multimedia development for using in the classroom environment at universities. The research object is application of mobile ICT to the educational process. The research subject is methods of applying mobile testing systems and mobile means to conduct practical classes at the University. The studies of Ukrainian and foreign researchers dedicated to the question of using mobile ICT for the university educational process were analyzed. Mobile testing systems are defined as a variety of mobile software support aimed firstly to measure students’ academic results, which enables to automatize the process of both current and final control through applying modern testing means, and secondly to intensify the educational process comprehensively. It is found that mobile means of multimedia development are to fulfill the principles of multimedia, space vicinity, time contiguity, coherence, modality, excessiveness, personalization, interactivity signalization and individual distinctions. The authors have developed the methods of applying mobile testing systems by taking Plickers system, as the one providing the opportunity to arrange a rapid feedback between a lecturer and both an academic group and an individual student. The system also allows conducting mobile surveys, in-class general questioning and instant control of students’ attendance. The authors have developed methods of applying mobile tools of multimedia development through using augmented reality. The comparative assessment of functionality of mobile testing systems and mobile means of developing augmented reality multimedia was held. Efficiency of the developed technology was experimentally tested and confirmed.
Lipschitz constrained GANs via boundedness and continuity

Abstract One of the challenges in the study of generative adversarial networks (GANs) is the difficulty of its performance control. Lipschitz constraint is essential in guaranteeing training stability for GANs. Although heuristic methods such as weight clipping, gradient penalty and spectral normalization have been proposed to enforce Lipschitz constraint, it is still difficult to achieve a solution that is both practically effective and theoretically provably satisfying a Lipschitz constraint. In this paper, we introduce the boundedness and continuity (BC) conditions to enforce the Lipschitz constraint on the discriminator functions of GANs. We prove theoretically that GANs with discriminators meeting the BC conditions satisfy the Lipschitz constraint. We present a practically very effective implementation of a GAN based on a convolutional neural network (CNN) by forcing the CNN to satisfy the BC conditions (BC–GAN). We show that as compared to recent techniques including gradient penalty and spectral normalization, BC–GANs have not only better performances but also lower computational complexity.
Automated Tax Return Verification with Blockchain Technology

One of the most important phases of the tax system is the verification process which ensures that the taxpayers are paying their proper share. In Bangladesh, the verification process is fully manual; hence, it is time consuming, tedious, error-prone and easy to manipulate. The taxpayers can file taxes with incorrect or falsified information, and manual verification can overlook that information. In this paper, we have proposed an automated tax verification system using Blockchain technology which will reduce the potential corruption in many folds. Update or deletion on any data in Blockchain system is not allowed. The inherent security feature of Blockchain makes the data secure, temper-proof and difficult to hack. In the proposed architecture, in addition to securing the taxpayers’ information on these private Blockchain systems, the taxpayers’ filed tax returns are verified instantaneously on the fly. In this verification process, the system retrieves the taxpayers’ information from the employers’, financial institutions’ and all other appropriate institutions’ private Blockchain systems. To further reduce fraudulent financial schemes, as a part of the verification process, it compares the current year’s taxable income, tax exemption, etc., of the taxpayers with their previous year’s information.