Reviewerly/exHarmony huggingface dataset:
Legal regulation of the implementation of secondary vocational education programs in the context of the COVID-19 pandemic

The purpose of the study is to identify problems in the legal regulation of the activities of the secondary vocational education system in the context of the COVID-19 pandemic and to formulate proposals for their leveling. The subject of study is individual institutions of educational legislation in the field of secondary vocational education, the implementation of which is directly related to the activities of educational organizations in the context of countering the spread of COVID-19. The key issues of the use of e-learning and distance learning technologies, the provision of paid educational services in the changed conditions are considered; practical issues, as well as the positions on them of the federal executive authorities and judicial practice. The main problems associated with the legal regulation of the education system in a pandemic are presented. Conclusions are made about the inadmissibility of giving assignments during the holidays to students; that the use of e-learning and distance learning technologies does not change the form of education, while maintaining the volume of compulsory classes of a teacher with students; on situations of application of these technologies, requiring free provision of students with the necessary equipment; on the need to change the content of the contract for the provision of paid educational services, as well as on the inadmissibility of increasing its cost.
The Problem of Understanding in Russian Idealism of the Late 19<sup>th</sup> and Early 20<sup>th</sup> Centuries: V.S. Solovyov and S.N. Trubetskoy

The paper examines the doctrine of understanding in Russian idealism from the late 19 th to early 20 th century. The author discusses the main ontological and epistemological concepts in the philosophy of V.S. Solovyov and his follower S.N. Trubetskoy. The paper offers a historical and philosophical reconstruction of the concept of understanding based on the analysis of V.S. Solovyov's Lectures on Divine Humanity and S.N. Trubetskoy's work On the Nature of Human Consciousness . According to Solovyov, the study of understanding is possible if we presume an unconditional principle that premises being as the One. Based on this premise, it is demonstrated that the unconditional principle allows to know everything without having knowledge of each subject separately, and this, in turn, is understanding process. Trubetskoy's theory of understanding is premised on the concept of soborny (gathered) mind, which defines mind as joint knowledge about all spheres of existence. The article shows that the main position shared by the Russian thinkers is the unconditional principle serving as the basis for life and mind without which the existence of either the universal or the particular is impossible. The author concludes that such a principle is a cognitive relation to the world, which unconditionality is the condition for understanding the reality as indivisible. Understanding, as presented by V.S. Solovyov and S.N. Trubetskoy, is the true understanding of the unity of existence in all its manifestations, that is, a synthesis of the sensual, rational, moral, and aesthetic.
Developing Kamishibai and Hologram Multimedia for Environmental Education at Elementary School

A Japanese method in teaching at classroom show good result by implementing Kamishibai.On the other hand, technology is inseparable from daily life.Computer-based learning media innovations are fast and diverse, ranging from 2D animation to 3D environments.The hologram is one example of 3D object visualization to deliver the learning material.Based on Kamishibai's opportunity and hologram multimedia utilization to enhance the environmental education teaching activity, research about the adaptation of 3D pyramid hologram for teaching environmental education in elementary school is proposed.Firstly, the kamishibai model for teaching environmental education from Japan is explored and modified to fit Indonesia's condition.The kamishibai and hologram multimedia utilization in teaching environmental education has been experimented with in class.The results of learning kamishibai and multimedia holograms show that students will improve their abilities in environmental problems.The students lead the literacy and caring attitudes were following their level of knowledge and skills as well as their attitudes about caring about disposing of garbage in its place, learning to clean sewerage at school, learning to farm in the yard of schools, and understand how plants exist in school gardens.
Multi-level Chunk-based Constituent-to-Dependency Treebank Transformation for Tibetan Dependency Parsing

Dependency parsing is an important task for Natural Language Processing (NLP). However, a mature parser requires a large treebank for training, which is still extremely costly to create. Tibetan is a kind of extremely low-resource language for NLP, there is no available Tibetan dependency treebank, which is currently obtained by manual annotation. Furthermore, there are few related kinds of research on the construction of treebank. We propose a novel method of multi-level chunk-based syntactic parsing to complete constituent-to-dependency treebank conversion for Tibetan under scarce conditions. Our method mines more dependencies of Tibetan sentences, builds a high-quality Tibetan dependency tree corpus, and makes fuller use of the inherent laws of the language itself. We train the dependency parsing models on the dependency treebank obtained by the preliminary transformation. The model achieves 86.5% accuracy, 96% LAS, and 97.85% UAS, which exceeds the optimal results of existing conversion methods. The experimental results show that our method has the potential to use a low-resource setting, which means we not only solve the problem of scarce Tibetan dependency treebank but also avoid needless manual annotation. The method embodies the regularity of strong knowledge-guided linguistic analysis methods, which is of great significance to promote the research of Tibetan information processing.
Gypsum Boards Reinforced with Cotton Dust Fiber

Abstract This research was aimed to study the production of gypsum boards mixed with cotton dust fiber for construction purposes. The cotton dust fiber 5, 10, 15, and 20% by weight was mixed with gypsum plaster and cast in the size 300 × 400 × 9 cubic millimeters. The water content was added 77 % by weight of the mixture. The ultimate strength, modulus of rupture, and weight of the gypsum board were investigated. The results showed that the ultimate strength and modulus of rupture of the gypsum boards without cotton dust fiber were 168.78 Newton and 3.66 Mega Pascal, respectively, which met the criteria for standard gypsum board under TIS 219-2524. However, the ultimate strength and modulus of rupture of the gypsum board mixed with cotton dust fiber were lower than the standard gypsum board. The weight of the gypsum board was decreased when the cotton dust fiber content was increased. It was found that the appropriate ratio of gypsum plaster and cotton dust was 90:10 incorporated with 6 wt. % PVA. The ultimate strength and modulus of rupture of the appropriate gypsum board were 135.31 N and 3.13 MPa, which was the criteria standard gypsum board products (TIS. 219-2524). The weight of the appropriate gypsum board was lighter than the gypsum board without cotton dust fiber addition about 289.75 grams (21 percent). Therefore the gypsum board mixed with cotton dust fiber could be developed for the construction, ceiling, and wall.
Privacy Protection for Medical Image Management Based on Blockchain

With the rapid development of medical research and the advance of information technology, Electronic Health Records (EHR) has attracted considerable attention in recent years due to its characteristics of easy storage, convenient access, and good shareability. The medical image is one of the most frequently used data format in the EHR data, which is closely relevant to patient personal data and involves many highly sensitive information such as patient names, ID numbers, diagnostic information and telephone numbers. A recent survey reveals that about 24.3 million medical images have been leaked from 50 countries all over the world. Moreover, these medical images can be easily modified or lost during the transmission, which seriously hinders the EHR data sharing. Blockchain is an emerging technology which integrates reliable storage, high security and non-tamperability. In this paper, we propose a privacy protection model that integrates data desensitization and multiple signatures based on blockchain to protect the patient’s medical image data. We evaluate the performance of our proposed method through extensive experiments, the results show that our proposed method achieves desirable performance.
Knowledge based information system for detecting early eye diseases

Abstract The eyes are very important senses in human life. If the eyes experience interference, it will be fatal to human life. Although each type of eye disease has its own specific characteristics and symptoms, early detection with a deeper recognition of eye disease symptoms should be done. Knowledge-based information systems are systems that attempt to convert human knowledge into computers so that computers can solve problems as is usually done by experts. produce conclusions or goals. The aim of this study is to design applications for early detection of fourteen types of eye diseases. This detection uses a knowledge-based system that uses the Forward Chaining method. This study produced an output in the form of the possibility of the disease the user suffered based on fifty-three selected symptoms. This system indicates how much confidence the user has in the symptoms of possible eye diseases.
Hyperchaotic Encryption Algorithm Based on Joseph Traversal and Bit Plane Reconstruction

Image encryption is an effective technology to protect digital image security. This paper designs an image encryption algorithm based on Joseph problem and bit plane reconstruction. The encryption algorithm adopts the classical scrambling diffusion structure. By using the principle of Joseph problem, pixel points of encrypted image are moved to different positions to realize pixel scrambling of the image. The pseudo-random sequence generated by hyperchaos is used to scramble the different bit-planes of the encrypted image. Finally, DNA is used to encode the image and cipher feedback is used for diffusion. The simulation results show that the algorithm can encrypt different types of images into evenly distributed encrypted images. The security analysis shows that the algorithm has high key sensitivity and can resist various attacks.
DEVELOPMENT OF DECISION-SUPPORT ALGORITHMS FOR COMMODITY MANAGEMENT

Abstract. The purpose of this paper is to improve the commodity management of retail chains by developing theoretical provisions of decision support system of retail chain commodity management (DSS RCM). Trading networks buy goods directly from manufacturers or large wholesalers, place these goods in warehouses, organize their distribution to stores. Commodity managers should decide how to provide all stores of the network in timely manner with the most profitable product every day. The objective of the DSS RCM is to maximize the daily trading margin, per each euro invested in commodities taking into account restrictions on commodity resources and shelf space. The DSS RCM have to prepare bills of lading and orders for inbound logistics, distribution and re-distribution of commodities within the network. Decision support uses data available in any retail software. The calculation algorithms are simple and effective to ensure the necessary and sufficient accuracy with the time and hardware limitations and without significant investments in hardware and staff qualifications update. Algorithms calculate the optimal solution based on the objective function (for example, profit maximization) and restrictions (for example, inventory level, store sales, profit from sales of a given product). The paper systematized the approaches of other scientists to solve the problem of product management in retail. Author analyze the strengths and weaknesses of those approaches for purpose to find the optimal approach for retail chains. So the aim of this study is to develop theoretical basis of decision support system of retail chain commodity management (DSS RCM). The proposed method of optimization of commodity assets in retail based on their mathematical modeling and calculating of the consolidated profitability ratio. Research results are limited to homogeneous product retail chains (e.g., clothing or footwear). By using proposed algorithms calculations could be in real-time mode in the database for tens (hundreds) of thousands items in the product range (for example, when selling clothes and shoes, a unique combination of model, color and size forms a separate item in the product range) in hundreds (thousands) of stores. Therefore, it could be hundreds of millions of combinations items-stores (Big Data). Keywords: retail chain, commodity management, decision support, sales analytics. JEL Classification F17, L81, M31 Formulas: 9; fig.: 0; tabl.: 1; bibl.: 39.
Cloud Computing Value Chains: Research from the Operations Management Perspective

Problem definition: Cloud computing has been recognized as a critical driver of Information-Technology enabled innovations. The Operations Management (OM) community, however, has not been exposed enough to the essential operations problems that arise from the management of cloud value chains. In this paper, we examine recent research on cloud value chains and explore future research opportunities from an OM perspective. In particular, we focus on major operations management challenges facing a cloud provider in three problem domains: (1) cloud computing resource management, (2) pricing in the cloud computing marketplaces, and (3) capacity planning and management of cloud supply chains.Methodology and Results: We describe prevalent business models and management practices in the cloud value chains, discuss recent research from OM that falls into each of the three problem domains mentioned above, and point out opportunities for future research. We note that cloud computing operations are driven by demand that exhibits the distinct characteristics including complex workflow, demand redundancy, multi- featured characteristics, multi-dimensional resource requirement, and non-stationarity. On the supply side, cloud computing operations also exhibit distinct characteristics including heterogeneous resources, packing constraints, bundled supply, technology risks, and cost uncertainty. These characteristics of demand and supply are not all prevalent in other operations.Managerial Implications: Cloud computing operations not only share many features with classic OM problems, but also bring new challenges and innovative business models. Thus, OM tools and research have the potential to provide vital insights into cloud computing operations and impact management practices in the cloud industry, which in turn can stimulate much innovative research from the OM perspective.
Classification of security quality level using Fuzzy Logic study case: Balikpapan City

Abstract A liveable city is a city with a comfortable environment that gives its residents a sense of perfection to live and ease to do various activities. Balikpapan is one of major cities whose has the third-largest population in the province of East Kalimantan consisting of 6 districts namely, South Balikpapan, East Balikpapan, North Balikpapan, West Balikpapan, Central Balikpapan, and Balikpapan City. The consequence that arises from the large population is the increase in crime that affects the liveability in terms of security quality. In this study, Mamdani Fuzzy Logic is used to investigate the level of security quality of each sub-district in this city based on the crime figures. Moreover, the types of criminalities are categorized into four kinds, namely fraud, theft, immoral violence, and other crimes. Based on the result, it was found that the order of sub-districts with the best level of security is Central Balikpapan which is followed by Northern and Eastern Balikpapan with fairly good security status. Meanwhile, sub-districts with lax security are Western Balikpapan, Balikpapan City and Southern Balikpapan.
Adaptive Neuro-Fuzzy Inference System-Based Yield Forecast Using Climatic Variables

Crop yield is affected by climate, prevailing during crop season and inputs applied. As such modeling, the cause and effect relationship between yield and these factors could provide an approach for pre-harvest yield forecast. Prediction of impacts of Climate Change (CC) on crop yields requires a model and its parameters, how crops respond to climate. Predictions from various models often disagree with the climatic variables and its impact. A common method is used to quantify impacts of CC is statistical models trained on historical yields and some simplified measurements of weather parameters, such as growing season average temperature and precipitation. CC is a really big apprehension to the entire world. Its direct impact on crop growth and yield is very important to understand. In the present study, the Fuzzy logic crop yield model was developed by considering different climatic variables. Temperature, rainfall, evaporation, humidity parameters were considered for the crop yield model. The model was developed by considering the 15-year crop yield data and the same period for the climatic variables. The triangular membership function is being adopted in the fuzzy model. In this study, a fuzzy rule-based system (FRBS) using the Takagi Sugeno-Kang approach has been used for developing the crop yield model. Model is validated by the coefficient of correlation and found that there is more than 0.9 coefficient of correlation between observed and evaluated yield.
A virtual imprint of the artificial neural networks

In the direct simulation Monte Carlo method and multi-phase flow calculations, structured and unstructured grid systems for complex geometries necessitate the time-consuming process of searching a large number of cells to position target particles.
Accurate Cardinality Estimation of Co-occurring Words Using Suffix Trees

Estimating the cost of a query plan is one of the hardest problems in query optimization. This includes cardinality estimates of string search patterns, of multi-word strings like phrases or text snippets in particular. At first sight, suffix trees address this problem. To curb the memory usage of a suffix tree, one often prunes the tree to a certain depth. But this pruning method “takes away” more information from long strings than from short ones. This problem is particularly severe with sets of long strings, the setting studied here. In this article, we propose respective pruning techniques. Our approaches remove characters with low information value. The various variants determine a character’s information value in different ways, e.g., by using conditional entropy with respect to previous characters in the string. Our experiments show that, in contrast to the well-known pruned suffix tree, our technique provides significantly better estimations when the tree size is reduced by 60% or less. Due to the redundancy of natural language, our pruning techniques yield hardly any error for tree-size reductions of up to 50%.
Classification using Deep Learning based on selection optimizing counseling data

Abstract Counseling is professional assistance given by counselors to clients directly, to solve problems and improve their behavior. In the future. The discussion of the problem in question is in-depth which concerns important matters about the client, broad covering various aspects of the client’s problem, and specifically to alleviate the client’s pressing problems. The Support Vector Machine and Multi Layer Perceptron algorithm is applied to classify counseling data provided by professionals to clients with an accuracy of 86.39%. Data from Vocational Research in Malang City in 2018. To improve the optimal accuracy of the type of counseling data classification, this study proposes an algorithm optimization feature for the Deep Learning algorithm. The results showed a significant increase in accuracy. The value of classification accuracy in counseling data generated by the Deep Learning algorithm has increased to 97.37% with a better level of accuracy.
Blind Quality Assessment for High Dynamic Range Video Systems

Compared with traditional imaging methods, high dynamic range (HDR) imaging techniques can provide the visual contents with wider dynamic range, higher contrast, and richer detail information. In HDR video system, distortion is inevitably produced during capturing, processing and coding HDR video, leading to the degradation of visual quality. Therefore, how to objectively assess the quality of HDR video is an important problem to be solved. This paper proposes a new blind HDR video quality assessment method based on luminance partition and motion perception, considering the perceptual impacts of texture and motion information in HDR video. Firstly, the distorted HDR video is divided into groups of frames (GoFs), and tensor decomposition is performed on each GoF to obtain its static information map containing the main spatial information and the corresponding motion information maps containing the main temporal information of HDR video. Considering the different visual perception to texture details of the bright, middle and dark regions in the distorted HDR video, the luminance partition is performed on the GoF's static information map to obtain the brightness, middle and dark regions, and the texture features are extracted from the different luminance partitioned regions to form the global and local texture perception feature sets. Then, considering the temporal perception, the motion information map is used to extract motion perception features and structure perception features. The extracted features are fused in the temporal to form the final feature set used to predict the quality of distorted HDR videos. Moreover, a subjective database of HDR videos is established to verify the effectiveness of the proposed method. Experimental results show that the proposed method can evaluate the HDR video quality accurately, and has good consistency with human visual perception.
Klasterisasi Dana Bantuan Pada Program Keluarga Harapan (PKH) Menggunakan Metode K-Means

The Family of Hope Program (PKH) is a program that aims to reduce poverty and improve the quality of human resources. Optimizing the provision of assistance in accordance with the expectations of those in need. Data on the poor or integrated social welfare data is needed as a reference for grouping. This study aims to make it easier for the selection team to provide assistance in accordance with the predetermined criteria whether or not they deserve to receive the assistance. The data used in the study is data from 2019. The data processing in this study uses the K-Means Clustering method with 3 clusters, namely Cluster 1 (C1) Nearly Poor Households (RTHM), Cluster 2 (C2) Poor Households (RTM), Cluster 3 (C3) Very Poor Households (RTSM). The results of the clustering process with 2 iterations state that for Cluster 1 the amount of data is, for Cluster 2 the amount of data, and for Cluster 3 the amount of data. So this research is very helpful in relocating targeted assistance according to the family hope cluster.