Reviewerly/exHarmony huggingface dataset:
Experience of Realizing the State Program of the Russian Federation “Development of Education” (by the Example of Teaching the Buryat Language in Pre-School Educational Institutions of the Republic of Buryatia)

The paper examines the process of the Buryat language teaching in pre-school educational institutions of the Republic of Buryatia from the viewpoint of realization of the state program “Development of Education”. The article describes the measures implemented in the sphere of pre-school education within the framework of the proposed federal projects. Scientific originality of the study lies in the fact that the author evaluates quality of the pre-school Buryat language education and analyses the measures implemented within the framework of the proposed federal projects. The research findings are as follows: the author shows how measures of regional bodies in the sphere of the pre-school Buryat language education correlate with the program provisions.
Preventing Enclave Malware with Intermediate Enclaves on Semi-honest Cloud Platforms

Threats to data processing in the cloud computing environment are increasing with emerging cloud applications. Intel Software Guard Extensions (SGX) implements a Trusted Execution Environment (TEE) through hardware-based isolation, which can enhance the security of cloud applications by providing a separate secure space that does not reveal its interior. A trusted memory area isolated by SGX, so-called enclave, protects the confidential code and data from any other software, including highly privileged system software. However, the adversary also benefits from isolation by inserting malicious code into the enclave where the system cannot detect it. Existing studies suggest various measures to deal with enclave malware, but these are based on an underlying assumption that the system must be trusted, which does not apply to the semi-honest cloud platform. To this end, we propose a novel method, named Interclave, that protects the system from enclave malware in the cloud computing environment without trusting any component other than the intermediate enclave. Interclave forces every ECALL and OCALL of a suspicious enclave to be executed through an intermediate enclave. This prevents the malicious behavior of enclave malware such as code-reuse attacks that modify the stack or register values associated with the program's execution address. Interclave stores and restores all stack and register values before and after the execution of the suspicious enclave, ensuring the integrity of these values. Our implementation is done with slight code modifications which occupy only a little storage capacity without complicated hardware changes.
Tolerance Satisfiability Sequences for Image Similarity

We propose a solution to a fundamental problem of content based image retrieval (CBIR), "How to calculate the degree of similarity among a pair of images". This article introduces a novel technique called Tolerance Satisfiablity Sequence (TSS) which is based on the concept of tolerance space and near set theory. In this work, images are divided into different visual element (objects) and affinity is calculated between the corresponding object of the two images. Each object is further divided into sub-objects that are mapped to each other under the constraints of tolerance satisfiability. The maximal satisfying set of sub objects (between the two parent objects) is found. This maximal set of sub-object is (termed as TSS) compared with the parent object i.e. the ratio of the cardinality of maximum satisfying set to the cardinality of the parent object is calculated and this gives the quantified similarity index termed as Tolerant Satisfiable Similarity Metric (TSSM). Initially, we verified that proposed approach is metric as well as pseudo-metric and later it is compared with popular image nearness metrics, viz., Hausdorff and Structural Similarity Index (SSIM). It is experimentally proved that the TSSM outperformed them in visual acuity.
Preparedness of Higher Education Institutions for the Digitization of the Educational Process

At the modern stage of development of information technology, the most important aspect of efficiency of higher education in Russia is preparedness of the higher education system in general and professors at higher education institutions in particular for working with digital technologies. Research findings presented in this paper allow assessing such indicators as the level of digital literacy of university professors, their mastering of ICT competencies, as well as the level of development of the digital educational environment of Russian higher education institutions, including the degree of availability of digital equipment in higher education institutions, the use of educational software products and online education services, the degree of development of training and online education technologies. This research allows making several significant conclusions which are necessary for the development of the higher education system in Russia in the context of the digital economy and the development of necessary methodological tools for the comprehensive assessment of preparedness of higher education institutions for the introduction of digital education technologies and products.
AMSR-E soil moisture inversion based on GRNN neural network

Passive microwave remote sensing data and measured soil moisture content in AMSR-E (Advanced Microwave scanning Radiometer - Earth Observing System) had been adopted for hilly region of Central Sichuan province from 2006 to 2010. Using Tbv6.9, Tbv10.7, Tbv18.7 and Tbh36.5 as input factors, A GRNN neural network was established to retrieve soil water content, and the correlation coefficient between the simulated value and the spatial and temporal sequence of measured soil water was calculated. The results showed that the GRNN neural network's correlation coefficient inversion of soil moisture and the measured soil moisture is relatively higher than the one of soil moisture retrieved by the equation inversion of standard atmospheric transmission and the measured soil moisture. Thus using AMSR-E data based on GRNN neural network model to simulate the soil moisture is feasible.
A Research on Multi-Scale Breakpoint Repair Algorithm oflnfrared Vein Image Based on Vein Direction

Extracted veins often have a large number of breakpoints, losing the continuity of veins after vein image binarization with image algorithms such as OTSU algorithm. In this paper, a multi-scale breakpoint repair algorithm based on vein direction is proposed. Firstly, the overall vein direction is obtained by using the designed multi-direction matrix on preprocessed image. Then the vein image is binarized by bidirectional valley recognition algorithm. Finally, the vein breakpoint is connected by the proposed multi-scale vein breakpoint repair algorithm. The experimental results show that the multi-scale breakpoint repair algorithm based on vein direction can repair breakpoints well and improve the continuity of veins.
Analysis of Low Probability of Detection Capability for Chaotic Standard Map-Based FH-OFDMA System

A frequency hopping orthogonal frequency division multiple access (FH-OFDMA) can provide low probability of detection (LPD) and anti-jamming capabilities to users against adversary detectors. To obtain an extreme LPD capability that cannot be provided by the basic symbol-by-symbol (SBS)-based FH pattern, we proposed two FH patterns, namely chaotic standard map (CSM) and cat map for FH-OFDMA systems. In our previous work, through analysis of complexity to regenerate the transmitted symbol sequence, at the point of adversary detectors, we found that the CSM had a lower probability of intercept than the cat map and SBS. It is possible when a detector already knows symbol and frame structures, and the detector has been synchronized to the FH-OFDMA system. Unlike the previous work, here, we analyze whether the CSM provides greater LPD capability than the cat map and SBS by detection probability using spectrum sensing technique. We analyze the detection probability of the CSM and provide detection probabilities of the cat map and SBS compared to the CSM. Based on our analysis of the detection probability and numerical results, it is evident that the CSM provides greater LPD capability than both the cat map and SBS-based FH-OFDMA systems.
Text Style Transfer Using DRG Framework of Combined Retrieval Strategy

Text style transfer is a task that changes the stylistic attribute while preserving content information. It can be used for various applications such as intelligent chatbot and literary works. Based on the recently appeared model, namely General Style Transformer(GST), we propose a new variant of it by replacing one of its step. The GST basically follows three steps: delete, retrieve, and generative (DRG) steps. The DRG framework is a pipeline structure, so errors of the deletion step might propagate to its following steps. To mitigate this, we modify the retrieval step to take the result sentence of the deletion step and the raw sentence as well, and results of the two sentences. It combines the results of the two sentences, so it has a chance to alleviate some errors generated by the deletion step. By experimental results with Yelp dataset, we prove the effectiveness of our approach.
Recognition of Handwritten Indian Trilingual City Names

For postal documents in India, irrespective of the fact that it bears a PIN or not, writing the destination city name is compulsory and without which a document will never be able to reach its desired location. Although in many countries like USA we find a fully automated system of postal services, lot of research works are still needed to be pursued for a multi lingual and a multi script country like India to develop such a postal system. Due to a large number of varieties in hand writings of individuals and applications of more than one language at the time of writing, recognition of the accurate destination city names written in Indian postal documents becomes a very challenging task. From our study on existing works on recognition of Indian city names we concluded that as on date research work on Indian city name recognition has been carried out for Indian city names written in English, Bangla, Hindi, Tamil and Gurumukhi languages out of the twenty three languages which officially exist in India and as a consequence there is definitely appreciable scope of research in this area. In this paper, we present our work dealing with recognition of handwritten trilingual Indian city names where we have considered a minimum of thirty samples in each case where the same city name is written in English, Bangla and Hindi. Bangla and Hindi are two of the most popular Indian languages and English though being a non Indian language is used very frequently in most of the postal documents in India even today. Our proposed work was carried on 2700 handwritten city names, each 900 of which were written in English, Bangla and Hindi respectively. Our proposed system was able to correctly recognize 2592 city names and thereby we achieved 96% accuracy using CNN.
Application of Network Intelligence in College English Teaching

Traditional college English teaching methods can’t meet the requirements of educational advancement any more, and can’t cultivate the quality of students in all respects. During the reform process, the implementation of network teaching has achieved great results, enriched teaching ways and ensured the quality of teaching. This article aims to study the application of network intelligence in college English teaching. At first, it elaborates network artificial intelligence based on literature research. Then it introduces the application of network teaching in English and explains its advantages. Finally, it finds out the main problems in college English instructing and puts forward solutions. The results of experiment in this paper show that in comparison with traditional English teaching, the effect of college English instructing has been improved and the students’ English scores have been greatly improved.
Novel Approach of Class Incremental Learning on Internet of Things (IoT) Framework

Class incremental learning and Internet of Things (IoT) are related but have not yet been extensively integrated. With the exponential rise in the number of IoT devices, the total amount of data produced is massive, thus it is infeasible to send all the data directly to the cloud. Training deep learning models in an incremental manner can help saving the training time and reduce the computational load required to train the model. In this work, we develop and demonstrate an incremental class learning system using only features of images to reduce large memory footprints in comparison with using raw images. Our experiments show that the proposed method achieves similar accuracy within a shorter time frame. We also develop a distributed training method of convolutional neural networks (CNNs) between an IoT edge device and a cloud. The IoT edge device carries out feature extraction to reduce the transmission load to the cloud which also eases the training cost. The cloud carries out the classifier training.
Construction of Agricultural Knowledge Service Platform for Scientific and Technological Innovation

In order to meet the information service demand of agricultural sci-tech innovation under the big data environment, the global agricultural information resources are gathered and integrated, and the agricultural knowledge service platform is established by using data acquisition, text mining, knowledge organization and other technologies, providing the functions of global agricultural information dynamic monitoring, intelligent retrieval, resource personalized recommendation, etc. The platform provides personalized in-depth knowledge services for multi-level, multi-type and multi-disciplinary users, so as to effectively promote agricultural science and technology innovation.
A SURVEY ON THE ACOUSTIC ENVIRONMENT IN ACADEMIC LIBRARY LEARNING COMMONS

Learning commons (LC) is a space provided in a library to support active learning for users. LC is typically equipped with information technology equipment and space for group discussion so that users can utilize them for exploring information and making discussions. On the other hand, since LC is a space where people are encouraged discussing, acoustic problems may occur such as noise intrusion into other quiet zones or disturbance on concentration of neighboring users in learning due to loud conversation of other groups. However, not many years have passed since the introduction of LC and therefore there are few studies on the acoustic environment of LC.
A Qualitative Study on the Experience of Military Life and Ministry of Korean Christian Female Military Pastors

The purpose of this study was to understand who female military pastors are in the Korean military and examine their military lives and ministry experiences through in-depth interviews and qualitative analysis. Six of the eight female military pastors currently working participated in this study. Seven major themes were identified: motivation to apply, role confusion and ambiguity, token women in the Korean military, perceived barriers or bias, equality experience, lack of network or mentor, and work and family balance. This study was helpful for obtaining a deeper understanding of female military pastors who exist as a minority in the Korean military. This study also provides basic information for the seminary education of future female military pastors and policies for female leaders working in the Korean military.
Harmonic Means between TF-IDF and Angle of Similarity to Identify Prospective Applicants in a Recruitment Setting

Recruitment industry is better and bigger than ever. There is no denying that technology plays a major role in helping recruiters evolve and adopt with the pace of recruitment on a global scale. With the increasing population, the demand for manpower has been relative to the growth and challenging needs of recruiters; be it online or traditional way of outsourcing. In this study, we propose a combination of angle or similarity and term frequency–inverse document frequency to easily classify prospective job applicants. The results show that the two models are relative to each other, value-wise and harmonic means. Their values are synchronized to a certain extent based on our query. This is helpful because recruiters may save a lot of time in classifying prospective applicants. It can also be concluded that harmonic similarity is viable in combining the two models. As a future work, it is possible to develop a full featured application to be deployed in a production setting.
STUDY OF PECULIARITIES OF PEDAGOGICAL CONTROL OF PUPILS 'TRAINING

The aim of the work was to study the features of pedagogical control of schoolchildren's learning in a modern general education school. Research methods: theoretical: a review of psychological and pedagogical literature on research issues. The advantages of the system for assessing the degree of training are that each subsequent parameter is qualitatively higher than the previous one, and the system as a whole assesses the growth of knowledge and skills of students and the proposed parameters are convenient to operate in practice during the current assessment of knowledge, skills and abilities of students. The disadvantage of the system - the last parameter - "transfer" - characterizes as the student's ability to solve problems of a heuristic and creative type, ie. this stage requires detailing. The teaching function of control is to determine the content, techniques and methods of control, which are educational in nature. Any controlling task, except for the controlling function, trains students in the implementation of specific educational actions, ensuring a more solid mastery of these actions. The controlling task includes an element of novelty in the informative and substantive terms, providing an expansion of the cognitive horizons of students, developing and increasing the learning value of control. It is difficult to eliminate the subjective element of pedagogical control due to various circumstances: the designation of learning outcomes is rather arbitrary: knowledge, abilities, skills, assimilation, academic performance, etc .; methods of direct measurement of educational activity have not been developed, and it is judged indirectly by the answers, by the actions of students. Pedagogical control is an important component of the educational process that influences the result, the course of training and education. This problem is one of the most urgent in teaching practice.
Detection of Image Forgery Using Information Standard Method With SVM

Abstract Depending on various features, image forgery is now used to enhance and restore images to make them more significant, whereas image forgery is performed by vulnerable people to generate fake truths by tampering images. From large (image) databases, a specified number of images forgery, new mechanism will have implemented to detect systems which is mainly based on two procedures. The first procedure relies on extract features of image through filters (LBP, Gabor, PCA and DCT) to find out its efficiency evaluated using some Statistics Measurements(Mean, Variance) and the second procedure relies on several quality metrics in optimization processes which (PSNR, MSE, AIC) have been calculated to which results in better quality compared to other image by combining selected filters with SVM, and, through experimental test, it was found that this proposed method of detect forgery technique is powerful than the classical detect system. The proposed methodology gives the better accuracy up to 98.5%, to detect and extract all type of forgery, with image enhancement capabilities.
A Gyártóberendezések és rendszerek tantárgy projekt alapú oktatása – tapasztalatok: Project-Based teaching of Manufacturing Systems and Equipments - Experiences

Based on positive experiences and based on positive feedback from our students we introduced a project-based learning method in both semesters of our subject - Manufacturing Equipment and Systems. The first semester assignments were focused on positioning a workpiece with the given conditions and constraints. During the second semester project teams formed by the students were working on a project of a single purpose machine tool.KivonatA kedvező tapasztalatokbol kiindulva es a pozitiv hallgatoi visszajelzesekre alapozva a Gyartoberendezesek es rendszerek tantargyban mindket felevben bevezettuk a projekt alapu feladatmegoldast. Az első felevi feladatok temaja egy adott munkadarab pozicionalasa volt a megadott feltetelekkel es korlatozo parameterek figyelembevetelevel. A masodik felev egy celgep tervezesi feladat volt, az egy evvel ezelőtti tanulsagok es tapasztalatok hasznositasaval.
Automated Condition Monitoring Using Artificial Intelligence

Machine maintenance is a major part of total operating costs for manufacturing and production plants, hence, having a cost-effective maintenance strategy is key to success. Most standard solutions operate on a time-driven approach where repairs or replacements are carried out after a given amount of time. This results in healthy machines being replaced unnecessarily incurring losses in both time, resource, and parts. Predictive maintenance utilises sensors capable of collecting real-time operating data and analytical techniques to predict when a machine will require maintenance. This research develops a vibration analysis tool that can detect and differentiate a machines operating conditions without any human analysis or intervention demonstrated via a case study. By using spectrograms in conjunction with image recognition and unsupervised learning, the tool is capable of being applied to many different machines without modification. Results show that features extracted using convolutional auto-encoding and t-SNE, are capable of being clustered in an unsupervised manner accurately based on case study test conditions. Future work looks at the application of this tool into a real-time data stream that can determine operating conditions in real-time.
Analysis and Prediction of Myanmar Prosodic Unit Boundary

Myanmar belongs to the Lolo-Burmese sub-branch of the Tibeto-Burmese branch of the Sino-Tibetan language family and is a tonal language. In the front-end text analysis of speech synthesis, the prosodic structure analysis and unit's boundary prediction are crucial to the naturalness of speech synthesis. In order to improve the naturalness of Myanmar speech synthesis, this paper studies prosodic features and prosodic unit boundary prediction. The size of prosodic units and the duration of syllables before and after their boundaries have been studied in this paper. To realize automatic prosodic unit boundaries labeling, a method of labeling based on the combination of word segmentation text and silence duration is proposed. Based on BiLSTM-CRF model, we also have designed and implemented a method to predict the boundaries of prosodic units from Myanmar text. Finally, the boundary prediction results are applied to the speech synthesis system based on HMM to evaluate its naturalness. The experimental results show that our method of automatic prosodic boundary labeling and prosodic unit boundary prediction can improve the naturalness of speech synthesis.
Infrared Image Enhancement Using Convolution Matrices

Advancement in infrared detector technology from 1st generation to 3rd generation infrared focal plane arrays (IRFPA) has put a lot of emphasis on the development of high performance image processing for improved and efficient infrared imaging systems. It requires image enhancement module to provide better detection capability. The most efficient way to enhance an image is by converting it into a convolution matrix and then carry out the enhancement pixel by pixel. In this paper, comparisons of two algorithms have been presented to enhance the infrared (IR) images. Implementation of Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE) which helps in increasing image clarity has been also discussed for IR images.