Reviewerly/exHarmony huggingface dataset:
Human-computer interactive teaching model based on fuzzy set and BP neural network

As the main channel of human-computer communication, multimedia education technology can realize the virtualization of education and multi-dimensional information. In this paper, the authors design a human-computer interactive English experience teaching based on fuzzy set and BP neural network. W ith the development of intelligent education, the improvement of English teaching quality is particularly important, especially the effectiveness of classroom teaching. The construction of interactive education system has changed the traditional classroom teaching mode and made it possible for individual autonomous learning. This English teaching system is an E-learning platform based on network updating. The E-learning system has the following advantages: (1) E-learning media is easy to update, can provide more teaching content, and provide convenience for teaching. (2) E-learning interaction can stimulate learning interest; (3) E-learning is rich in educational materials and easy to set teaching objectives. (4) Using E-learning's human-computer interaction function, we can strengthen the supervision of education and teaching process.
Mix‐flow scheduling using deep reinforcement learning for software‐defined data‐center networks

For a mix‐flow scenario in software‐defined data‐center networks, how to simultaneously achieve the different performance requirements of the different types of flows is a considerable challenge. This paper proposes a mix‐flow scheduling scheme based on deep reinforcement learning (DRL). This paper establishes three private link sets for three types of flows. Then, DRL is employed to adaptively and intelligently allocate bandwidth for each type of flow according to the traffic variations across time and space. A novel metric is designed as a function of DRL's reward to guide the process of simultaneously maximizing the deadline meet rate for mice flows (MF) and minimizing the flow completion time for elephant flows. Within these three private link sets, three flow‐scheduling strategies (ie, priority‐based allocation for MF, stable matching‐based allocation for elephant flows with unknown sizes, and proportion‐based allocation for elephant flows with known sizes) are employed. A simulation demonstrates the effectiveness of the proposed scheme compared with previous methods (Fincher and pFabric). DRL‐Flow's overhead also is minimal to satisfy the scalability well and is deployable in a large‐scale network.
Wireless sensor network based monitoring system for precision agriculture in Uzbekistan

The last decades the WSN technology has been adopted by more and more scientific fields for accurate and effective monitoring of climate phenomena like air pollution, destruction phenomena like landslides, etc. It has been widely used in agriculture for field monitoring. WSN is an emerging technology, which through the research in the labs and the real deployments has been proved to be a significant and valuable tool for scientists to explore another world which is behind the various environmental phenomena using tiny sensor nodes In this article, "Expert Advisory System" was developed to improve the productivity of farmers, save their time and improve the efficiency of the crops. The system monitors real-time crop fields using wireless sensor networks and provides the necessary information to farmers via the Internet. The farmer will be required to undertake the necessary remedial action on the basis of the information received. It’s also provided that the simulation of WSN in Contiki Simulator tool. Moreover, the queing model for WSN to also considered in this work.
&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$\beta$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-Dropout: A Unified Dropout

Dropout is an effective regularization method for deep learning tasks. Several variants of dropout based on sampling with different distributions have been proposed individually and have shown good generalization performance on various learning tasks. Among these variants, the canonical Bernoulli dropout is a discrete method, while the uniform dropout and the Gaussian dropout are continuous dropout methods. When facing a new learning task, one must make a decision on which method is more suitable, which is somehow unnatural and inconvenient. In this paper, we attempt to change the selection problem to a parameter tuning problem by proposing a general form of dropout, β-dropout, to unify the discrete dropout with continuous dropout. We show that by adjusting the shape parameter β, the β-dropout can yield the Bernoulli dropout, uniform dropout, and approximate Gaussian dropout. Furthermore, it can obtain continuous regularization strength, which paves the way for self-adaptive dropout regularization. As a first attempt, we propose a self-adaptive β-dropout, in which the parameter β is tuned automatically following a pre-designed strategy. The β-dropout is tested extensively on the MNIST, CIFAR-10, SVHN, NORB, and ILSVRC-12 datasets to investigate its superior performance. The results show that the β-dropout can conduct finer control of its regularization strength, therefore obtaining better performance.
Open Online Assessment Management System Platform and Instrument to Enhance the Information, Media, and ICT Literacy Skills of 21st Century Learners

Information literacy (IL), media literacy (ML), and information communication and technology literacy (ICT Literacy: ICTL) are some of the most important skills for 21st century learning and which help promote other skills, including life and career skills and learning and innovation skills. This kind of learning allows students to connect as a learning network without barriers or borders. It fully supports the use of equipment and technology to develop the skills necessary for life. The purpose of this research study is two-fold: first, to develop a research-based Open Online Assessment Management System (OOAMS), and second to develop the standardized measurement for an assessment of IL, ML, and ICTL for higher education learners. The sample group consists of 2,300 higher educa-tion learners and the methodology was divided into 3 phases: (1) developing the conceptual framework, definition, and features of IL, ML, and ICTL for higher education learners; (2) examining requirements, designing wireframes, and de-veloping an OOAMS for higher education students; and (3) testing the quality of OOAMS for IL, ML, and ICTL using exploratory factor analysis (EFA) and confirmation factor analysis (CFA) statistical methods. The research result found that, first, the specific features of the system included development using PHP and MySQL database and the design to interact with users with a responsive UI framework. The system is compatible with MOOCs and the Open edX platform, or can be used as a stand-alone application. It can create, share, copy, and extend both Likert scales and rubrics evaluation forms. It can also generate reports in both CSV and PDF formats. Secondly, the results of this research provided a standardized measurement for assessing IL (49 items), ML (63 items), and ICTL (69 items). Also, it can be improved or enhanced for online learning in a ubiqui-tous learning context such as e-learning, blended learning, virtual learning and MOOCs effectively.
3D geochemical modeling for subsurface targets of Dashui Au deposit in Western Qinling (China)

Three dimensional (3D) geochemical modeling was used to identify deposit scale exploration targets combining the geological (110 boreholes and 6 tunnels with 709 assay borehole samples) dataset and 374 litho-geochemical samples in the Dashui Au deposit (China). The Dashui Au deposit, located in the Western Qinling orogenic belt, is a magmatic-hydrothermal type large Au deposit discovered in recent years. The methodologies are summarized as follows: (1) the 3D geological bodies (ore bodies, granitic stocks/dikes, fault fractures, and alteration zones) modeling using GOCAD software. (2) 3D deposit-scale geological modeling in mining zones combination the deposit-scale plane maps, cross sections, longitudinal sections, tunnel planes, borehole datasets, and the range of 3D model is 2.3 × 0.55 × 0.6 km3. (3) The ordinary kriging and discrete smooth interpolation (DSI) interpolation methods were used to analyze the distribution trend of Au grade features combined with borehole and the primary litho-geochemical halos dataset, and the Cu/Pb ratio model is analyzed and constructed by metallogenic model/rule using primary geochemical halos data with DSI interpolation method. (4) The concentration-volume (C–V) fractal method is used to analyze the anomaly classification of grade and Cu/Pb ratio in 3D space using GeoCube software. The results show as follows: (1) the high Au grade value zones are associated with ore-bearing intrusion and the NW- and NE-trending fault structures; (2) the high Au value blocks of Cu/Pb ratio can be used to delineate ore-bearing magmatic fluids channel and mineralization zones; (3) the integrated exploration criteria are Cu/Pb ratio, the NE- and NW-trending faults, and intrusions in the Dashui deposits, the potential targets at depth are located in the southwest and southeast zones under the mining levels of No. 2, No. 170, No. 20, and No. 35 Au orebodies, and the Au resource is >172 t (the depth is <1000 m) in the study area.
The Effect of Accounting Information System on Organizational Performance in Jordanian Industrial SMEs: The Mediating Role of Knowledge Management

While adopting knowledge management can improve firm performance, it is determined by use of accounting information system.Furthermore, it plays mediating role in the relationship between accounting information system and organization performance.This paper employs regression analysis using SPSS 20 to investigate causal relationships among the variables and to examine the mediation of knowledge management.The empirical findings from a survey of 350 employees in SMEs industrial firms in Jordan confirmed that knowledge management exert a mediating effect on the relationship between accounting information system and organization performance.The main implication of the findings for firms is that use accounting information system working are more likely to achieve higher performance, as a result of strengthened knowledge management.The findings offer evidence on relationships among the variables as well as mediating role of knowledge management in effect of accounting information system on firm performance.This paper is useful to management researchers as well as business managers.
Deep recursive up-down sampling networks for single image super-resolution

Single image super-resolution (SISR) technology can reconstruct a high-resolution (HR) image from the corresponding low-resolution (LR) image. The emergence of deep learning pushes SISR to a new level. The successful application of the recursive network motivates us to explore a more efficient SISR method. In this paper, we propose the deep recursive up-down sampling networks (DRUDN) for SISR. In DRUDN, an original LR image is directly fed without extra interpolation. Then, we use the sophisticated recursive up-down sampling blocks (RUDB) to learn the complex mapping between the LR image and the HR image. At the reconstruction part, the feature map is up-scaled to the ideal size by a de-convolutional layer. Extensive experiments demonstrate that DRUDN outperforms the state-of-the-art methods in both subjective effects and objective evaluation.
Hybrid Preprocessing Method for Support Vector Machine for Classification of Imbalanced Cerebral Infarction Datasets

Cerebral infarction is one of the causes of ischemic stroke in the brain, and machine learning can be used in the detection of cerebral infarction in the brain. In diagnosing the presence of cerebral infarction in the brain, machine learning is used because it is not enough just to use a CT scan to diagnose. Support vector machine (SVM) is a machine learning method that is known for its high accuracy value. However, SVM can produce less optimal results if the data used is imbalanced. If imbalanced data is used, the resulting model will be biased. Therefore, this study uses a hybrid preprocessing method for SVM on the classification of an imbalanced cerebral infarction dataset obtained from the Department of Radiology at Dr. Cipto Mangunkusumo Hospital. This method is a combination of several sampling methods that deal with the problem of imbalanced data and utilizes undersampling and oversampling techniques in combination with SVM. Oversampling modifying the infarction dataset through the duplication of data with a small number of classes to be balanced with a large number of data classes. While undersampling reducing data with a large number of classes to be balanced with a smaller number of data classes. Undersampling and Oversampling are combined into a hybrid method. This method is a hybrid method of the undersampling and oversampling that can be used in SVM. The results of hybrid method using SVM will be compared with the undersampling and oversampling using SVM, individually. And SVM method without preprocessing the imbalanced dataset. The accuracy of the proposed method reached 94% in our evaluations for SVM using a hybrid preprocessing method.
The classification of the modern arabic poetry using machine learning

In recent years, working on text classification and analysis of Arabic texts using machine learning has seen some progress, but most of this research has not focused on Arabic poetry. Because of some difficulties in the analysis of Arabic poetry, it was required the use of standard Arabic language on which “Al Arud”, the science of studying poetry is based. This paper presents an approach that uses machine learning for the classification of modern Arabic poetry into four types: love poems, Islamic poems, social poems, and political poems. Each of these species usually has features that indicate the class of the poem. Despite the challenges generated by the difficulty of the rules of the Arabic language on which this classification depends, we proposed a new automatic way of modern Arabic poems classification to solve these issues. The recommended method is suitable for the above-mentioned classes of poems. This study used Naïve Bayes, Support Vector Machines, and Linear Support Vector for the classification processes. Data preprocessing was an important step of the approach in this paper, as it increased the accuracy of the classification.
Dual Guided Loss for Ground-Based Cloud Classification in Weather Station Networks

In this paper, we propose a novel loss function named dual-guided loss (DGL) for ground-based cloud classification in weather station networks. The proposed DGL could integrate the knowledge of different convolutional neural networks (CNNs) in the process of optimization, which improves the discriminative ability of ground-based cloud feature representations. To this end, we add a modulation term into the DGL, which assigns large weights to the hard-classified ground-based cloud samples. As a result, the deep network is forced to pay more attention to these hard-classified samples, and therefore, the performance of the deep network gets improved. We demonstrate the effectiveness of the proposed DGL with the extensive experiments on two ground-based cloud datasets, and the experimental results of the DGL outperform the state-of-the-art methods.
All Pure Fermionic Non-Gaussian States Are Magic States for Matchgate Computations

Magic states were introduced in the context of Clifford circuits as a resource that elevates classically simulatable computations to quantum universal capability, while maintaining the same gate set. Here we study magic states in the context of matchgate (MG) circuits, where the notion becomes more subtle, as MGs are subject to locality constraints. Nevertheless a similar picture of gate-gadget constructions applies, and we show that every pure fermionic state which is non-Gaussian, i.e., which cannot be generated by MGs from a computational basis state, is a magic state for MG computations. This result has significance for prospective quantum computing implementation in view of the fact that MG circuit evolutions coincide with the quantum physical evolution of noninteracting fermions.
How journalists understand the threats and opportunities of new technologies: A study of security mind-sets and its implications for press freedom

This is a study on digital security and press freedom, specifically the freedom from surveillance and the freedom to private and secure communication. We study journalists’ understanding and awareness of digital security practices: their perception of risks and threats of surveillance, and also their awareness of tools and techniques to communicate securely. Based on an empirical study of journalists in Hong Kong, including those who regularly travel to China for work, we build out a theoretical framework for understanding digital security mind-sets of journalists. We found differences in journalists’ behavior depending on how novice or advanced their understanding of digital security are. Journalists with a novice security mind-set will take different actions that produce different outcomes and behavior than those with an advanced security mind-set. We argue that journalists with an advanced security mind-set are able to work on a larger and wider range of (sensitive) stories, suggesting they enjoy a higher degree of press freedom.
The evolution of IJHCS and CHI: A quantitative analysis

In this paper we focus on the International Journal of Human-Computer Studies (IJHCS) as a domain of analysis, to gain insights about its evolution in the past 50 years and what this evolution tells us about the research landscape associated with the journal. To this purpose we use techniques from the field of Science of Science and analyse the relevant scholarly data to identify a variety of phenomena, including significant geopolitical patterns, the key trends that emerge from a topic-centric analysis, and the insights that can be drawn from an analysis of citation data. Because the area of Human-Computer Interaction (HCI) has always been a central focus for IJHCS, we also include in the analysis the CHI conference, which is the premiere scientific venue in HCI. Analysing both venues provides more data points to our study and allows us to consider two alternative viewpoints on the evolution of HCI research.
A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading

Nowadays, Artificial Intelligence (AI) is changing our daily life in many application fields. Automatic trading has inspired a large number of field experts and scientists in developing innovative techniques and deploying cutting-edge technologies to trade different markets. In this context, cryptocurrency has given new interest in the application of AI techniques for predicting the future price of a financial asset. In this work Deep Reinforcement Learning is applied to trade bitcoin. More precisely, Double and Dueling Double Deep Q-learning Networks are compared over a period of almost four years. Two reward functions are also tested: Sharpe ratio and profit reward functions. The Double Deep Q-learning trading system based on Sharpe ratio reward function demonstrated to be the most profitable approach for trading bitcoin.
Deep Generative Adversarial Networks for Thin-Section Infant MR Image Reconstruction

Due to their high spatial resolution, thin-section magnetic resonance (MR) images serve as ideal medical images for brain structure investigation and brain surgery navigation.However, compared with the clinically widely used thick-section MR images, thin-section MR images are less available due to the imaging cost.Thin-section MR images of infants are even scarcer but are quite valuable for the study of human brain development.Therefore, we propose a method for the reconstruction of thin-section MR images from thick-section images.A two-stage reconstruction framework based on generative adversarial networks (GANs) and a convolutional neural network (CNN) is proposed to reconstruct thin-section MR images from thick-section images in the axial and sagittal planes.A 3D-Y-Net-GAN is first proposed to fuse MR images from the axial and sagittal planes and to achieve the first-stage thin-section reconstruction.A 3D-DenseU-Net followed by a stack of enhanced residual blocks is then proposed to provide further detail recalibrations and structural corrections in the sagittal plane.In this method, a comprehensive loss function is also proposed to help the networks capture more structural details.The reconstruction performance of the proposed method is compared with bicubic interpolation, sparse representation, and 3D-SRU-Net.Cross-validation based on 35 cases and independent testing based on two datasets with totally 114 cases reveal that, compared with the other three methods, the proposed method provides an average 23.5% improvement in peak signal-to-noise ratio (PSNR), 90.5% improvement in structural similarity (SSIM), and 21.5% improvement in normalized mutual information (NMI).The quantitative evaluation and visual inspection demonstrate that our proposed method outperforms those methods by reconstructing more realistic results with better structural details.
Intra-Balance Virtual Machine Placement for Effective Reduction in Energy Consumption and SLA Violation

Cloud computing has emerged as one of the most important technological revolutions globally.However, the rapid growth of cloud computing has imposed a massive financial burden and resulted in environmental side effects due to excessive energy consumption.The high power consumption is not only attributed to the size of data centers but also to the ineptitude of resource usage.Most of the extant research has focused on reducing power consumption by an aggressive VM consolidation, which leads to the violation of the service level agreement (SLA).Furthermore, the unbalanced resource consumption exacerbates the unavailable wasted resources that are referred to as unavailable resource fragmentation.In this paper, we propose the use of a balanced resources consumption algorithm called BRC-IBMMT in order to enhance the efficiency of resource consumption while achieving an acceptable balance between conflicting correlation objectives of power consumption as well as SLA violation.The extensive simulation results of different types of workload validate and lend credence to the significance of the proposed method in reducing power consumption and SLA violation of the cloud data center.
Large-Scale Traffic Congestion Prediction Based on the Symmetric Extreme Learning Machine Cluster Fast Learning Method

The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. Traditional neural network training takes a long time in addition to easily falling into the local optimal and overfitting. Accordingly, this inhibits the large-scale application of traffic prediction. On the basis of the theory of the extreme learning machine (ELM), the current paper puts forward a symmetric-ELM-cluster (S-ELM-Cluster) fast learning methodology. In this suggested methodology, the complex learning issue of large-scale data is transformed into different issues on small- and medium-scale data sets. Additionally, this methodology makes use of the extreme learning machine algorithm for the purpose of training the subprediction model on each different section of road, followed by establishing a congestion prediction model cluster for all the roads in the city. Together, this methodology fully exploits the benefits associated with the ELM algorithm in terms of accuracy over smaller subsets, high training speed, fewer parameters, and easy parallel acceleration for the realization of high-accuracy and high-efficiency large-scale traffic congestion data learning.