Reviewerly/exHarmony huggingface dataset:
Analysis of various transfer functions for binary owl search algorithm in feature selection problem

ABSTRACTOwl Search Algorithm (OSA) is a recently proposed nature-inspired meta-heuristic algorithm which is easily implementable and exhibits great potential for solving continuous optimization problems. In our earlier work, a binary version of owl search algorithm (BOSA), with transfer functions for mapping the continuous solution space into a binary one, has been developed and applied in optimal feature subset selection problem. In our preliminary simulation experiments, it was found that the performance of the solution depends on the type of transfer function used. In this work, an extensive analysis of various types of transfer functions and their respective effects on the selection of optimal feature subset has been studied by simulation experiments with multiple benchmark datasets. Transfer functions of three different families, S-shaped, V-shaped and quadratic, are used for designing eleven BOSA models, each of which is made by combining individual transfer function. The performances of the proposed wrapper based feature subset selection algorithm based on several BOSA models have been evaluated by simulation experiments with twenty datasets for finding out the best model. The best observed BOSA model has also been compared with other similar meta-heuristics algorithms for feature subset selection. Experimental results show that the feature subset selected by BOSA with quadratic transfer function produces the highest classification accuracy with the minimum number of selected features compared to other algorithms.
High Efficiency Continuous-Variable Quantum Key Distribution Based on Quasi-Cyclic LDPC Codes

Information reconciliation in the continuousvariable quantum key distribution (CVQKD) plays a vital role on affecting secret key rate as well as maximum transmission distance. It is necessary to search good error-correcting codes in order to improve the performance of reconciliation efficiency. In this work, we construct Quasi-Cyclic (QC) low-density paritycheck (LDPC) codes based on LDPC codes in the digital video broadcasting second generation terrestrial (DVB-T2) standard, and then combine these codes with information reconciliation in the CVQKD system, which can extremely ameliorate the performance of secret key rate and maximum transmission distance. The simulation results show that our proposed QC-LDPC codes can achieve higher reconciliation efficiency of 93.05% compare to DVB-T2 LDPC codes, which means our proposed QC-LDPC codes can obtain better performance of secret key rate and longer maximum transmission distance.
Target to Source Coordinate-Wise Adaptation of Pre-trained Models

Domain adaptation aims to alleviate the gap between source and target data drawn from different distributions. Most of the related works seek either for a latent space where source and target data share the same distribution, or for a transformation of the source distribution to match the target one. In this paper, we introduce an original scenario where the former trained source model is directly reused on target data, requiring only finding a transformation from the target domain to the source domain. As a first approach to tackle this problem, we propose a greedy coordinate-wise transformation leveraging on optimal transport. Beyond being fully independent of the model initially learned on the source data, the achieved transformation has the following three assets: scalability, interpretability and feature-type free (continuous and/or categorical). Our procedure is numerically evaluated on various real datasets, including domain adaptation benchmarks and also a challenging fraud detection dataset with very imbalanced classes. Interestingly, we observe that transforming a small subset of the target features leads to accuracies competitive with “classical” domain adaptation methods.
Pre-interpolation Loss Behavior in Neural Networks

When training neural networks as classifiers, it is common to observe an increase in average test loss while still maintaining or improving the overall classification accuracy on the same dataset. In spite of the ubiquity of this phenomenon, it has not been well studied and is often dismissively attributed to an increase in borderline correct classifications. We present an empirical investigation that shows how this phenomenon is actually a result of the differential manner by which test samples are processed. In essence: test loss does not increase overall, but only for a small minority of samples. Large representational capacities allow losses to decrease for the vast majority of test samples at the cost of extreme increases for others. This effect seems to be mainly caused by increased parameter values relating to the correctly processed sample features. Our findings contribute to the practical understanding of a common behavior of deep neural networks. We also discuss the implications of this work for network optimization and generalization.
Intelligent Digital Currency and Dynamic Coding Service System Based on Internet of Things Technology

The amazing rise of digital currency is not only favored by investors but also attractive to lawbreakers for its anonymity and decentralization. This paper mainly discusses the intelligent digital currency and dynamic coding service system based on Internet of Things technology. In this paper, the RDCAR algorithm is used to realize the routing discovery process of the wireless network. When the intermediate node receives the RREQ message, first of all, to avoid the loop, it checks whether the same RREQ message has been introduced. If it has received it, it will discard it. Otherwise, it will cache the message and attach its own neighbor node list to the signal-to-noise ratio of the channel link, update the RREQ message, and broadcast it. The payment cipher is managed by the bank. When the user opens an account, the bank registers and sends it to the user. The key is generated by the algorithm chip, and the public key is kept in the bank background server. When the bill is delivered to the bank, the bank inputs all the elements on the bill on the counter terminal and transmits it to the verification machine for verification through the bank network. If the verification is correct, it indicates that the bill is indeed issued by the customer, and all bill elements are correct, and payment can be made. The node operation protocol of public chain and alliance chain maintains the operation of the Internet of Things system. The nodes of alliance chain generate new blocks according to the interval of 30 s. When the node fails to complete the block generation within 30 s, it will rotate to the next node. The mkfile command is used to generate 16b, 1 KB, 1 MB, and 1 GB files as input. The peak speed of the encoding service system is about 370 mb/s. The results show that the system designed in this study is robust and suitable for complex trading environment.
Integrated Management of an Eco-clean Small Watershed in the Qinba Mountainous Area

Zhang, T.; Shen, Y., and Zhang, D., 2020. Integrated management of an eco-clean small watershed in the Qinba Mountainous Area. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 51–55. Coconut Creek (Florida), ISSN 0749-0208.An ecologically clean small watershed of Qinba Mountain is important to protect this water source of the South–North Water Diversion Project and to reinforce the ability to achieve sustainable development. A solution project on engineering technology and management measures is presented to develop an ecologically clean small watershed and living water environment in the Qinba Mountain Area. Some technologies and integrated measures were presented for protection of the water source, sewage treatment in rural drainages, and protection against agricultural surface pollution. The methodology and results of the ecologically clean small watershed of Qinba Mountain could be applied to other sites around the world that are affected by these extreme conditions.
Extracting Effective Image Attributes with Refined Universal Detection

Recently, image attributes containing high-level semantic information have been widely used in computer vision tasks, including visual recognition and image captioning. Existing attribute extraction methods map visual concepts to the probabilities of frequently-used words by directly using Convolutional Neural Networks (CNNs). Typically, two main problems exist in those methods. First, words of different parts of speech (POSs) are handled in the same way, but non-nominal words can hardly be mapped to visual regions through CNNs only. Second, synonymous nominal words are treated as independent and different words, in which similarities are ignored. In this paper, a novel Refined Universal Detection (RUDet) method is proposed to solve these two problems. Specifically, a Refinement (RF) module is designed to extract refined attributes of non-nominal words based on the attributes of nominal words and visual features. In addition, a Word Tree (WT) module is constructed to integrate synonymous nouns, which ensures that similar words hold similar and more accurate probabilities. Moreover, a Feature Enhancement (FE) module is adopted to enhance the ability to mine different visual concepts in different scales. Experiments conducted on the large-scale Microsoft (MS) COCO dataset illustrate the effectiveness of our proposed method.
RETRACTED: Research on nano-CT medical image processing based on embedded system

Embedded based medical image processing of clinical pictures is important and large. The appearance and widespread use of classified CT -Image for clinical imaging, expanding interest in robotization and quantitative research. The middle, gathering data that is unknown to the human viewer and fully understanding the results to avoid deceptive ends. By gathering real information on different frameworks, new applications to unravel current issues. Computed tomography (CT) uses an X-beam cylinder and an identifier board mounted around a hub where the CT -Image or Normal image is placed. By rotating the cylinder and the indicator together, images are taken at different points. These accurate estimates are adequately given, and the overall premise of CT is universally useful in this report. In this method, CT images and examines the primer tests performed by the creators. It is divided into a few sections that examine different themes related to Nano-CT
Socio-economic determinants of the transformation of the education system in Russia

Abstract. The purpose of this study is to draw the attention of the pedagogical community to the comprehension and understanding of the causes and essence of transformations taking place in the field of education of Russia at the present stage. We justified the deterministic nature of changes in the field of education by the peculiarities of building economic relations in the state. The transition of the Russian economy to a market system of relations with private property with means of production predetermined the subsequent transformations in the domestic education system. In the logic of market relations, education is considered as a service that should generate profit. This provision, according to us, is one of the starting points for transformations that have begun in the educational sphere. In the era of the industrial revolution, education became the locomotive for the development of science and technology, which, in turn, contributed to the economic efficiency of production processes, which favorably affected the growth of incomes of owners of industrial capital. Over time, scientific and technological progress, due to objective laws, began to lead to a negative effect for representatives of capital – a decrease in the rate of return. The current situation predetermined the artificial slowdown of scientific and technological progress and led to a review by the political and financial elites of the economically developed countries of the place and role of education in the state system. We considered essential transformations that take place in the domestic education system and gave our vision of the causes that determined these changes.
Research on Cloud Storage Platform Technology Based on Hadoop

Abstract In order to solve the problem of high cost of traditional data calculation and storage, and difficulty in writing parallel programs, this article summarizes the core technology of Hadoop, and uses Hadoop distributed processing technology and virtualization technology to design and build a cloud computing storage platform. The experiment proves that, compared with the traditional single-machine computing method and the physical machine cluster computing method, the platform has higher performance, and the resource utilization rate has also been improved.
A Method of Pedestrian Detection Based on Improved CNN in Single-frame Infrared Images

We proposed an improved method of pedestrian detection in infrared images based on the LeNet-7 system,to address the problems of large computation and low detection rates in traditional methods based on a full convolution neural network.The system consists of three convolution layers and three pooling layers.The trail selection method with the smallest error rate is used to determine the parameters of each layer,while the BU-TIV database,established by Boston University,is used to train the system.Firstly,the Object Tracking and Classification in and Beyond the Visible Spectrum(OTCBVS)and Terravic Motion IR Database,established by Ohio State University,are used to test images.Then,the region of interest(ROI)is obtained by vertical and horizontal projection with adaptive thresholds.Finally,the ROI is input into the trained system for testing.Experiments on three test sets demonstrate that the proposed method has good recognition ability.Compared with different experimental methods,the proposed method can effectively improve the detection rate.
Performance Enhancement of Image Stitching Process Under Bound Energy Aided Feature Matching and Varying Noise Environments

Image mosaicing is a method that combines several images or pictures of the superposing field of view to create a panoramic high-resolution picture. In the field of medical imagery, satellite data, computer vision, military automatic target recognition can be seen the importance of image mosaicing. The present domains of studies in computer vision, computer graphics and photo graphics are image stitching and video stitching. The registration of images includes five primary phases: feature detection and description; matching feature; rejection of outliers; transformation function derivation; image replication. Stitching images from specific scenes is a difficult job when images can be picked up under different noise. In this paper, we examine an algorithm for seamless stitching of images in order to resolve all such problems by employing dehazing methods to the collected images, and before defining image features and bound energy characteristics that match image-based features of the SIFT-Scale Invariant Feature Transform. The proposed method experimentation is compared with the conventional methods of stitching of image using squared distance to match the feature. The proposed seamless stitching technique is assessed on the basis of the metrics, HSGV and VSGV. The analysis of this stitching algorithm aims to minimize the amount of computation time and discrepancies in the final stitched results obtained.
Reducing the Influence of the Cognitive Miser Phenomenon and Cognitive Biases on the Selection Process in Exploratory Research Using a Reflective Approach

The aim of the study was to show how the reflective approach helps to reduce the influence of the cognitive phenomenon and cognitive biases on the selection process in exploratory research. For this, it was supposed to use the visualization of the subject area, for example, optimization methods, on any information medium. Visualization can relieve unnecessary stress on the brain, which is expensive to store all the details of a problem situation. To represent the subject area, the structure of the binary tree of the system of questions and answers was chosen. The structure and methodology for obtaining it allowed us to present knowledge in a form that completely removes the known cognitive biases - false attribution, zero risk effect, Stockholm shopping syndrome, Barnum effect, Dunning-Kruger effect, “false consensus effect” and the “cognitive curmudgeon” phenomenon. The effects were considered in the aspect of choosing one of the most suitable alternatives from the whole variety of alternatives. As a result of the study, it was found that structured knowledge devoid of cognitive distortions makes it possible to find the most powerful solution to a problem, draw up an invention formula and obtain a new solution method. An example of the emergence of the geometric programming method as a new optimization method was considered. The reflexive approach proposed by the authors allows obtaining structured information about the studied subject area without distortion and finding new solution methods for solving new problems. Now the subject area can be obtained without heuristic errors with significant savings in mental energy. This will significantly reduce research time, problem solving time and increase the accuracy of decisions.
Creativity as a Challenge in the EFL Classroom

Сreative skills have been a lot in the limelight of pedagogical research recently.Creative tasks have become an integral part of modern textbooks including those for foreign language studies.The authors link the relevance of students' creativity with the growing need of the Russian economy for individuals who can think outside the box, easily adapt to constant social changes and successfully communicate in multicultural professional environments.The research attempts to present the development of creative skills in students as a process associated with difficulties in its practical implementation.The article identifies popular tasks aimed at promoting creativity in EFL classes, lists the main difficulties that teachers face when using creative tasks, and suggests ways to overcome these difficulties.It is a descriptive and analytical study of the existing pedagogical research on the interpretation of the concepts of creativity, creative thinking and creative abilities, as well as on the use of creative tasks in the EFL classroom.The scope of analysis included review articles advocating for creative skills in modern society published in 2001-2019.In addition, action research articles published over the past three years describing the experience of using creative tasks when teaching foreign languages were analyzed.In the course of the study, a set of methods was utilized: review, observations in conversational English classes with students majoring in linguistics, an expert survey of foreign language teachers generated on the Google Forms platform, and interpretation of the data obtained.
Circle-Based Ratio Loss for Person Reidentification

Person reidentification (re-id) aims to recognize a specific pedestrian from uncrossed surveillance camera views. Most re-id methods perform the retrieval task by comparing the similarity of pedestrian features extracted from deep learning models. Therefore, learning a discriminative feature is critical for person reidentification. Many works supervise the model learning with one or more loss functions to obtain the discriminability of features. Softmax loss is one of the widely used loss functions in re-id. However, traditional softmax loss inherently focuses on the feature separability and fails to consider the compactness of within-class features. To further improve the accuracy of re-id, many efforts are conducted to shrink within-class discrepancy as well as between-class similarity. In this paper, we propose a circle-based ratio loss for person re-identification. Concretely, we normalize the learned features and classification weights to map these vectors in the hypersphere. Then we take the ratio of the maximal intraclass distance and the minimal interclass distance as an objective loss, so the between-class separability and within-class compactness can be optimized simultaneously during the training stage. Finally, with the joint training of an improved softmax loss and the ratio loss, the deep model could mine discriminative pedestrian information and learn robust features for the re-id task. Comprehensive experiments on three re-id benchmark datasets are carried out to illustrate the effectiveness of the proposed method. Specially, 83.12% mAP on Market-1501, 71.66% mAP on DukeMTMC-reID, and 66.26%/63.24% mAP on CUHK03 labeled/detected are achieved, respectively.
Calculating spin correlations with a quantum computer

We calculate spin correlation functions using IBM quantum processors, accessed online. We demonstrate the rotational invariance of the singlet state, interesting properties of the triplet states, and surprising features of a state of three entangled qubits. This exercise is ideal for remote learning and generates data with real quantum mechanical systems that are impractical to investigate in the local laboratory. Students learn a wide variety of skills, including calculation of multipartite spin correlation functions, design and analysis of quantum circuits, and remote measurement with real quantum processors.
Using virtual reality technologies to practice the algorithm of emergency medical care

The use of computer-based virtual reality (VR) technologies in the educational process is continuously increasing for a variety of professional activities. Medical education is one of the examples, where such efforts are particularly visible. Today’s VR products provide the maximum immersion into a virtual environment and active interaction with their components. These VR products are becoming increasingly more popular, even in comparison with pseudo-3D simulators. VR facilitates the development of the abilities for precision work as much as mechanical skills. In emergency medical care, it is particularly important to practice the skills requiring specialized knowledge and abilities. In this work, we discuss VR-simulators built to practice the algorithm of emergency medical assistance in a maximally realistic environment.
Patch-Based Holographic Image Sensing

Holographic representations of data enable distributed storage with progressive refinement when the stored packets of data are made available in any arbitrary order.In this paper, we propose and test patch-based transform coding holographic sensing of image data.Our proposal is optimized for progressive recovery under random order of retrieval of the stored data.The coding of the image patches relies on the design of distributed projections ensuring best image recovery, in terms of the 2 norm, at each retrieval stage.The performance depends only on the number of data packets that has been retrieved thus far.Several possible options to enhance the quality of the recovery while changing the size and number of data packets are discussed and tested.This leads us to examine several interesting bit-allocation and rate-distortion trade offs, highlighted for a set of natural images with ensemble estimated statistical properties.