Reviewerly/exHarmony huggingface dataset:
The non-ferrous and noble metals deposits’ position and manifestation in North Ossetia-Alania regarding the anomalies of potential fields

Abstract The relevance is determined by the need to create a scientific basis for the reassessment of known ore regions for the presence of highly liquid gold ores and the search for the new gold ore objects in previously unexplored territories to diversify the mining and metallurgical industries of North Ossetia (Alania). The purpose is to reveal the geophysical signs of non-ferrous and noble metals’ mineralization and potential gold content of ore-magmatic systems. The applied methods include the results analysis of the geophysical data use when performing the work within the prospective gold territories’ limits. A combined map construction of the gravitational and magnetic fields’ local anomalies based on the analysis of amplitude characteristics using a geophysical database of the Geological map of the Russian Federation on a scale of 1: 1,000,000 according to the sheet K-38.39 (Makhachkala) as well as the analysis of the spatial distribution of deposits and the manifestations of non-ferrous and noble metals relative to geophysical anomalies. The results obtained can be formulated as follows: the experience of using geophysical data when performing the work within the limits of the gold prospective territories in the Amur region is analyzed. The results of processing the data of the geophysical surveys results in the mountainous part of the North Ossetia (Alania) territory using modern methods for converting the potential fields are presented. The compiled maps of the gravitational and magnetic field’s local anomalies have been prepared. Within North Ossetia (Alania), spatial features of the deposits and ore occurrences of non-ferrous and noble metals with respect to anomalies of the gravitational and magnetic fields have been revealed. The confinement of the granitoid type ore-magmatic systems to negative anomalies of gravity has been established. It has been shown that potentially auriferous ore-magmatic systems are located within the limits of positive magnetic field anomalies.
Efficient Computational Models for assessment of Spatial Infection Features

Computational models can prove useful in investigation of numerous biological phenomena, including disease initiation and progression. Model experiments usually rely on simulation-based approaches, suitably formulated to maximize the utilization of available computing power. In this article, the approach is applied to spatial modelling of HIV infection and consequent damage to lymphatic tissue. With available clinical data, however sparse, a Bayesian sampling framework can be used to obtain estimates for key parameters. Our results indicate that HPC computing combined with efficient parallelization strategies, can provide detailed parameter estimates, potentially fostering the use of this kind of model in clinical practice.
Research on Safety Evaluation for Military Aviation Management Based on RBF Neural Network model

To raise the safety management level for military aviation management, this paper proposes a safety evaluation model based on RBF neural network is proposed for military aviation management. According to the operational characteristics of military aviation management system, an evaluation index system for military aviation management safety is built, and then on the basis RBF neural network are used to establish the safety evaluation model on military aviation management.
Entity Spatio-temporal Evolution Summarization in Knowledge Graphs

Knowledge graph has been growing in popularity with extensive applications in recent years, such as entity alignment, entity summarization, question answering, etc. However, the majority of research only focuses on one snapshot of the knowledge graph and neglects its dynamicity in nature, which often causes missing important information contained in other versions of the knowledge graph. Even worse, the incompleteness of the data in the knowledge graph is a challenge issue, which hinders the further utilization of the data. Considering that knowledge graph can evolve with time as well as the changing locations, it is necessary to summarize and integrate the entity temporal and spatial evolution information. To address this challenge, this paper pioneers to formulate the problem of entity spatio-temporal evolution summarization, capturing the entity evolution with time and location changes and integrating the data from two groups of various knowledge graphs. Further, we propose a two-stage approach: 1) generate entity temporal summarization and spatial summarization by utilizing the Triadic Formal Concept Analysis; 2) produce the spatio-temporal evolution summarization of the entity by adopting a fusion strategy. The obtained summarization results can be used to the visualization of the entity spatio-temporal evolution, data integration, and question answering.
Identity-Checking for Provable Data Possession Without Privacy Leakage

The user data stored in remote server should be assured intact. Remote data integrity checking scheme (RDIC) allows a third party auditor (TPA) to examine the integrity of data in cloud server. As we know, the user authentication is necessary in the cloud storage. Obviously, the user identity is sensitive as the user can be identified when it requests integrity checking. In this paper, we employ zero-knowledge undeniable signature to RDIC to tackle the privacy leakage. There is no evidence to show the user checking, thus the user privacy is protected.
A Spherical Search-based Archive Update Mechanism for Self-adaptive Differential Evolution

Recently, meta-heuristic algorithms have been researched in quantity. However, there are no algorithms applying to different problems effectively and having notable difference compared with other meta-heuristic algorithm. Evolutionary algorithms are classical algorithms of nature-inspired metaheuristic algorithms. Spherical evolution (SE) is one of metaheuristic algorithms proposed recently. SE uses a spherical search style rather than the traditional hypercube search style. Differential evolution (DE) is a classical algorithm of hypercube search style. In this paper, considering the strong exploration capability of SE and the powerful exploitation ability of DE, we synthesize two search style and propose a hybrid algorithm, and use archive to reuse the good individuals. The proposed algorithm exhibits better performance in comparison with other state-of-the-art algorithms in terms of robustness and effectiveness based on 30 benchmark functions of IEEE CEC2017.
Theme-Based Summarization for RDF Datasets

A growing number of RDF datasets are published on the web. These datasets can be viewed as graphs; querying, analyzing and visualizing such data graphs are critical challenges facing the applications willing to use them, especially when their size is important. Summarization can help addressing these challenges. In this paper, we present a summarization approach which exploits the underlying themes of an RDF graph, and builds a global summary from the theme summaries. To this end, we propose some node relevance metrics. We present some experiments to illustrate the effectiveness of our approach.
Investigation of String Motions of Bowed String Instruments: A Finite Element Approach

The bowed string motion of violin strings is examined using finite element analysis. The string is modeled as a bar with a finite number of elements, whereas the bow is one single node. The cause of the sound is the stick-slip effect, which occurs at steady bowing of a string. The contact area between the bow node and one string node is modeled as nodal contact. This work aims at investigating the Schelleng Diagram as well as the Guettler diagram. The Schelleng diagram shows bow force over bowing position and identifies the area of normal sound, i.e. the Helmholtz motion (von Helmholtz, Von den Tonempfindungen als physiologische Grundlage für die Theorie der Musik. Vieweg, Braunschweig, 1863), overtone sound and raucous sound, the latter depicts bow force over bow acceleration in order to find a proper parameter configuration for “perfect bowing starts”. The influence of different friction curves of the rosin are modeled and simulated as well, which has been tested and investigated extensively in the literature mainly experimentally, see e.g (Smith, Woodhouse, J Mech Phys Solids 48(8):1633–1681, 2000). An overall research of the finite-element modeling of bowed strings is started with this new simulation and modeling set-up. The numerical results will be compared to experimental results in future work.
A Robust Color Image Zero-Watermarking Based on SURF and DCT Features

For most of the current watermarking algorithms, a multi-function color image that can simultaneously perform copyright protection and content authentication is proposed as Dual watermarking algorithm. First, the original image is converted from RGB space to YUV space, and the SURF (speeded up robust feature) of the luminance component is extracted. The marker points, constructs a description vector according to the main direction of the feature point and splits it into two sub-vectors, respectively calculating the cosine clip between them and a reference vector Angle, construct a robust zero watermark sequence by comparing the size relationship of the angles; then 2 × 2 the original image, and the singular value norm of the sub-image block. The XOR operation is performed to generate fragile watermark information and embed it into the least significant bit of the image space. By calculating the original robust zero water at the time of copyright attribution. The correlation coefficient between the printed sequence and the watermark sequence extracted from the image to be detected is used as the basis for identification, and the content authentication is passed. Experimental results show that the proposed algorithm has good performance.
A Novel Encoding and Decoding Calibration Guiding Pathway for Pathological Image Analysis

Diagnostic pathology is the foundation and gold standard for identifying carcinomas, and the accurate quantification of pathological images can provide objective clues for pathologists to make more convincing diagnosis. Recently, the encoder-decoder architectures (EDAs) of convolutional neural networks (CNNs) are widely used in the analysis of pathological images. Despite the rapid innovation of EDAs, we have conducted extensive experiments based on a variety of commonly used EDAs, and found them cannot handle the interference of complex background in pathological images, making the architectures unable to focus on the regions of interest (RoIs), thus making the quantitative results unreliable. Therefore, we proposed a pathway named GLobal Bank (GLB) to guide the encoder and the decoder to extract more features of RoIs rather than the complex background. Sufficient experiments have proved that the architecture remoulded by GLB can achieve significant performance improvement, and the quantitative results are more accurate.
Emerging Technologies and Augmented Reality in the Development of Learning and Human Potential

This article establishes a frame of reference on the use of Augmented Reality (AR) applications in education, the significance of neurobiological maturation and enriched environments as conditioning factors for the development of human potential. A literary review of recent research on this subject was carried out, using as reference sources the bases of data: Google academic, Academic Search Complete, Lilacs and Scielo, using as keywords the terms: “Human Potential, Augmented Reality, Enriched Environments, Mental Processes”, in Spanish and English. The time period considered was the last five years. The search made it possible to identify multiple related works, but only the most significant were included in the review. The selection of papers included articles, research reports, monographs and book chapters. The analysis of the identified contents was carried out in function of a perspective of reflection, using for it the analytical-synthetic, inductive-deductive, historical-logical and dialectical methods. After the analysis of the information gathered, it was possible to conclude that the education of emotional learning capacity, sensory integration and significance in learning are neuropsychological conditions of learning mediated by Augmented Reality, considering challenges for education professionals the educational application of emerging technologies and the evaluation of their impact on the integral development of the student.
Facial Age Estimation Using Compact Facial Features

Facial age estimation studies have shown that the type of features used for face representation significantly impact the accuracy of age estimates. This work proposes a novel method of representing the face with compact facial features derived from extracted raw image pixels and Local Binary Patterns (LBP) for age estimation. The compact facial features are realized by exploiting the statistical properties of extracted facial features while aggregating over a whole feature set. The resulting compact feature set is of reduced dimensionality compared to the non-compact features. It also proves to retain relevant facial features as it achieves better age prediction accuracy than the non-compact features. An age group ranking model is also proposed which further reduces facial features dimensionality while improving age estimation accuracy. Experiments on the publicly-available FG-NET and Lifespan datasets gave a Mean Absolute Error (MAE) of 1.76 years and 3.29 years respectively, which are the lowest MAE so far reported on both datasets, to the best of our knowledge.
Landmark Detection Using Squeeze and Excitation Residual Neural Networks

Abstract Landmark Recognition is the technology that can anticipate landmark names straightforwardly from picture pixels, to help individuals better comprehend and sort out their photograph accumulations and for law enforcement officials to gauge the location of images submitted as evidence. Image classifications techniques have shown remarkable improvements over the last few years. To further improve computer vision technologies and methodologies, researchers are now concentrating on highly specific types of classification. Instead of classifying cats, cars or buildings, researchers are trying to classify among different types of landmarks - both natural and man-made. In the present age, a tremendous roadblock in landmark recognition research is the lack of large, well labelled datasets. To rectify this, Google has come up with the Google Landmark Recognition Dataset. The dataset contains 1.2 million images of 15000 categories of landmarks. For the project, a subset of Google Landmark Recognition dataset has been used. Various latest classification algorithms, like AlexNet, ResNet, SE-ResNet, VGG-16 and Inception v3 have been implemented to classify the images. Among them, the SE-ResNet architecture achieves the lowest loss value of 0.1822 and accuracy of 94.08% on the training set.
Fixation Points Estimation Based on Binocular Stereo Vision

This paper proposes a method of fixation points estimation combining the two-dimension mapping model with three-dimension stereo vision. The purpose of our study is to design an easy-to-use non-contact gaze tracking system under natural light. The method of two-dimension mapping is easy to achieve, however, it needs the user to keep the head being fixed. It could achieve higher estimation accuracy though it is still not easy for users to use the algorithm. To solve this problem, we have introduced the binocular cameras to calculate the pose of head and then add the related result into the result of 2D mapping to compensate the movement of head. The average error angles of gaze estimation in the case head fixed and head moving are 5.1°and 8.5°, respectively. This proposed method is easy to achieve and the experiment device is easy to mounted.
Measurement-Device-Independent Quantum Identity Authentication Based on Single Photon

To overcome the channel attacks of the detector side, a measurement-device-independent quantum identity authentication based on single photon is proposed. The participants authenticate the identity of each other based on a relay measurement result. The technique of measurement-device-independent enhances the distance in the communication. Moreover, the analysis results show that our scheme has the higher security under the general attacks.
АКСІОЛОГІЧНІ АСПЕКТИ МУЗИЧНО-ЕСТЕТИЧНОГО ВИХОВАННЯ У СЕРЕДНІЙ ОСВІТІ РЕСПУБЛІКИ КОРЕЯ

The importance is outlined of research both general and musical and aesthetic education experience in Republic of Korea as one of the most advanced countries of the modern world. The main focus is on axiology – targets and values that must be achieved through musical and aesthetic education. It is shown that the core of Confucianism tradition, inherent for Korea, is the ideology of aesthetic order in structure of society, nature and Universe, main component of which is musical one. According to this tradition education in Republic is general obligatory, and school music education in Korea preserves deep cultural traditions and is formed on the basis of social values, which is determined by the fact that the purpose of musical education as a discipline should ultimately coincide with national ideals. It is found that humanitarian subjects are included in the general secondary education programs since a children’s garden and school, finishing the university, that is dictated by social necessity for updating lines of intelligence by technically competent and highly by educated experts and is considered as expedient from the point of view of the public importance and demand in conditions of technical and social progress. The stages of development of musical and aesthetic education from the moment of a finding of independence are determined according to chronology of appearance of the acts, which regulated reforming of secondary education in Republic: The Emergency Education Act of February (1951), the First (1955), the Second (1963), the third (1973), the Fourth (1981), the Fifth (1987) and the Sixth (1992) Curriculum Guidelines for the Elementary School, The School Curriculum of the Republic of Korea. The detailed analysis of the School Curriculum from 2008 is carried out. It is demonstrated large volume and content of classroom hours of subjects of aesthetic, including musical and aesthetic direction – music, fine arts, literature, dramatic art, cinema – in the education plan of a comprehensive school in Republic of Korea. It is indicated that the purpose of musical and aesthetic education in the republic is not just the development of musical taste or professional skills in performing or creating music, but the formation of a morally perfect personality, which is guided by the laws of musical harmony and aesthetic order in any activity. Key words:  ideology of the aesthetic order, Curriculum Guidelines, School Curriculum of the Republic of Korea, musical schooling, musical and aesthetic education
Mode displaying mouse cursors for reducing input language mode confusion: Utility and user attitude evaluation

Users frequently commit typing errors due to input language mode confusion (ILMC) when switching between multiple languages. This study evaluated two mode display cursors (MDCs) as a potential solution to ILMC: the monochrome and color-changing MDCs. Two experiments were conducted to evaluate the two MDCs in different dimensions of usability. Experiment 1 evaluated the effects of MDCs on ILMC and related typing errors and Experiment 2, on self-reported preference, acceptance and annoyance. In Experiment 1, 45 participants were divided into three groups, that each performed multi-language typing using an ordinary mouse cursor and the two MDCs. The color-changing MDC was found to significantly reduce ILMC and related typing errors compared to the ordinary mouse cursor (p < .001). In Experiment 2, 30 participants performed three typical computer tasks using both MDCs. The mean annoyance rating was significantly larger for the color-changing MDC than for the monochrome MDC (p = .037).
Neural structural correspondence learning method based on features extending

Traditional neural structural correspondence learning only uses mutual information to select the pivot features, resulting in low quality of the selected pivot, which affects the cross-domain sentiment classification results. To solve this problem, we propose a features-extending neural structural correspondence learning model. The feature selection method are extended by the term frequency-inverse document frequency to obtain the pivot features that are more suitable for cross-domain sentiment classification. A mapping relationship between the pivot feature and the non-pivot feature is established by neural network, to reduce domain differences. The experiment results on Amazon product dataset show that the pivot features selected by the proposed method are more closely related to the sentiment polarity, and the average accuracy of the proposed method is 0.33% higher than that of traditional neural structural correspondence learning.
A Scale Adaptive Kernel Correlation Filter-Based Tracker with Optimized Update Strategy

Correlation filter-based trackers have achieved competitive results in the field of visual tracking, but the abilities to deal with the multi-scale and keep the tracking stability still need to be improved. In this article, the complex background tracking problems are studied based on the kernel correlation filter. In order to address the fixed size limitation, we present an improved scheme, which can not only realize scale adaption, but also retain the advantages of the original algorithm. In addition, the adaptive update learning rate of the model is achieved by using the peak sidelobe ratio as an indicator to measure the filter performance. Using the index, we can selectively reduce the model interference to low precision information. Moreover, we design a redetection mechanism that runs automatically when the tracking evaluation indicator is low. Compared with the radical model update methods, those conservative update strategies can effectively decrease the model pollution and ensure the tracking effect. Extensive verifications based on OTB-100 benchmark show that the optimized algorithm improves the accuracy, robustness, and generalization ability of target tracking.
Container Damage Identification Based on RP-FCN

In this paper, a new upsampling algorithm is proposed to Fusion Upsampling, and a new RP-FCN network is presented based on ResNeXt50, Pyramid Pooling, Fusion Upsampling and FCN framework. The information perception system, operation control system and dispatching command system of the fully automated wharf are gradually perfected, but the automatic inspection module of the container has not been built yet. In this paper, the RP-FCN network is applied to the identification of container damage in port, in order to add a luster to the increasingly perfect fully automated terminal system. The experiment shows that the effect of the Fusion Upsampling is better than the traditional bilinear interpolation and deconvolution, and the effect of RP-FCN is better than that of FCN.