Reviewerly/exHarmony huggingface dataset:
ELECTRONIC INFORMATION AND EDUCATIONAL ENVIRONMENT OF THE UNIVERSITY AS A MEANS OF ORGANIZING INDEPENDENT WORK OF STUDENTS

In the context of the modernization of Russian higher education, training should be built on a differentiated basis, have a problematic and developing character. The main purpose of higher education is to prepare a graduate who is not just knowledgeable but who knows how to apply his knowledge. In this regard, there is a need for continuous self-education and self-development, in the development of selection skills useful for students of professional development and improvement of information. At the same time the efforts of the teacher should be aimed at creating an environment focused on independence, interactivity, productivity of students' activities, an environment that provides an opportunity to form an individual educational experience of a student moving along their own educational path. To organize effective independent work of students it is advisable to create an integrated system for organizing this work based on the information and educational environment of the university. The article presents the experience of organizing independent work of students of the Arzamas branch of Nizhny NovgorodStateUniversity through the electronic information and educational environment of the university. The article describes the developed electronic courses in the disciplines of the psychological-pedagogical cycle, shows the importance of such areas of independent work of students when using the electronic educational environment of the university as the search for the necessary information; preparation for practical, laboratory, seminar classes; preparation for the survey, colloquium; test preparation; preparation for tests and other types of written test works; preparation for various competitions, contests and more.
An Efficient Deep Belief Network with Fuzzy Learning for Nonlinear System Modeling

A deep belief network (DBN) is one of the most effective ways to realize a deep learning technique, and has been attracting more and more attentions in nonlinear system modeling. However, it can not provide satisfactory results in learning speed and modeling accuracy, which is mainly caused by gradient diffusion. To address these problems and promote its development in cross-models, we propose an efficient DBN with a fuzzy neural network (DBFNN) for nonlinear system modeling. In this novel framework, DBN is considered as a pre-training technique to realize fast weight-initialization and to obtain a feature-representation vector. An FNN-based learning framework is developed for supervised modeling so as to eliminate the gradient diffusion issue, where its input happens to be the feature-representation vector. As a novel cross-model, DBFNN combines the advantages of both pre-training technique of DBN and an FNN model to improve nonlinear system modeling capability. A classical benchmark problem is used to demonstrate its superiority over existing single-models in learning speed and modeling accuracy.
PENGGUNAAN RODA PINTAR UNTUK KEMAMPUAN MEMBACA ANAK

THE USE OF SMART WHEELS FOR THE ABILITY TO READ CHILDRENWas used in the study of a wheel smart. A wheel smart is one of medium used to have been used to simplify the son of in reading. In the wheels of smart there are a board where the board first there are vowels, board second there are letters konsinan, and board third there are syllables. In a wheel smart later on can develop the ability the son of bank notes the nepali language, not only in a working meeting with about the concept of developing found herself listening to have been ready for speaking, read and write. Excess of media wheels smart this is easy and there are elements used game so students the joy of learning while playing. Using the smart are the expected to read well. Its objective in reading is so acquisition reading comprehension, Includes the contents of, and understand the meaning contained in reading materials. By reading someone be able to widen insight and knowledge. Methods used in this study this research is qualitative research methodology descriptive. Technique use the theory of data collection study experts taken of some books and scientific journal. The action used in this research was media uses the smart.
Visualization of path patterns in semantic graphs

Graphs with a large number of nodes and edges are difficult to visualize. Semantic graphs add to the challenge since their nodes and edges have types and this information must be mirrored in the visualization. A common approach to cope with this difficulty is to omit certain nodes and edges, displaying sub-graphs of smaller size. However, other transformations can be used to summarize semantic graphs and this research explores a particular one, both to reduce the graph?s size and to focus on its path patterns. A-graphs are a novel kind of graph designed to highlight path patterns using this kind of summarization. They are composed of a-nodes connected by a-edges, and these reflect respectively edges and nodes of the semantic graph. A-graphs trade the visualization of nodes and edges by the visualization of graph path patterns involving typed edges. Thus, they are targeted to users that require a deep understanding of the semantic graph it represents, in particular of its path patterns, rather than to users wanting to browse the semantic graph?s content. A-graphs help programmers querying the semantic graph or designers of semantic measures interested in using it as a semantic proxy. Hence, a-graphs are not expected to compete with other forms of semantic graph visualization but rather to be used as a complementary tool. This paper provides a precise definition both of a-graphs and of the mapping of semantic graphs into a-graphs. Their visualization is obtained with a-graphs diagrams. A web application to visualize and interact with these diagrams was implemented to validate the proposed approach. Diagrams of well-known semantic graphs are presented to illustrate the use of agraphs for discovering path patterns in different settings, such as the visualization of massive semantic graphs, the codification of SPARQL or the definition of semantic measures. The validation with large semantic graphs is the basis for a discussion on the insights provided by a-graphs on large semantic graphs: the difference between a-graphs and ontologies, path pattern visualization using a-graphs and the challenges posed by large semantic graphs.
NextGen Multi-Model Databases in Semantic Big Data Architectures

When semantic big data is managed in commercial settings, with time, the need may arise to integrate and interlink records from various data sources. In this vision paper, we discuss the potential of a new generation of multi-model database systems as data backends in such settings. Discussing a specific example scenario, we show how this family of database systems allows for agile and flexible schema management. We also identify open research challenges in generating sound triple-views from data stored in interlinked models, as a basis for SPARQL querying. We then conclude with a general overview of multi-model data management systems, to provide a wider scope of the problem domain.
ANALISA POLA PEMESANAN PRODUK MODERN TRADE INDEPENDENT DENGAN MENEREPAKAN ALGORITMA FP. GROWTH (STUDI KASUS: PT. ADAM DANI LESTARI)

Ordering in the field of sale and purchase of Ritail large and small ordinary happens to meet the needs of the store to reach consumers, it includes a list of goods and prices with different items and a variety of sizes, bookings made By looking at low or low stocks. Because the number of items that resulted in the booking is less efficient considering that each MTI store is tracking the different items, it is analyzed the pattern of the order based on the items simultaneously during the order by applying Algorithm. Growth, to easily order goods to be ordered and efficient. The results of the booking are at the same time by MTI, making it easier to order from the pattern analysis. Results obtained from the study of item recommendation is 2 or more items suggested through analysis using FP. Growth algorithm with confidence (confidence interval above 20%) and minimum 5% support. The Tools used Rapidminer 9.3 2019 generate tables and graphs from the FP. Growth algorithm and the Association Rule.Keywords: analysis, ordering products FP. Growth, RapidMiner 9.3.
Analysis Of Marketing Strategy In Students Face Masks Business Folta Repper: Folium Tamarin Rice Paper

In this era people prefer to use instant products. We found this problem during the survey for market opportunities. In addition there are several other problems such as: (1) The length of time in treatment. (2) Difficulty using the product. (3) Solid activity making it difficult to take the time to treat your face. We are here to solve this problem by creating a Folta Reper (Folium Tamarin Rice Paper) product. This product is an instant transparent mask that can be used anytime and anywhere by women and men. Using FOLTA REPER will not interfere with other activities. Made from natural ingredients: Sour leaves, honey and Rice Paper. The contents of FOLIUM TAMARIN RICE PAPER are: vitamin C, vitamin A, vitamin B1, vitamin B, vitamin B12 and vitamin D. The benefits of removing pimples, making faces brighter, preventing premature aging, lifting dead cells, lifting dead cells , whitening the face, cooling the skin, spreading the face minimizes pores, smooths the skin and removes smallpox scars on the face. The purpose of this study is to study and analyze "marketing strategies in marketing in the Folta Reper Student Mask business, so that they can find solutions to the problems faced. This study uses an analytical descriptive method by classifying strengths, weaknesses, opportunities and threats and establishing marketing strategies that match the marketing mix. The conclusion of this study is the identification of strengths, weaknesses, opportunities and threats possessed by the product and the intensification of several business strategies through a marketing mix approach.
A post-quantum UC-commitment scheme in the global random oracle model from code-based assumptions

<p style='text-indent:20px;'>In this work, we propose a post-quantum UC-commitment scheme in the Global Random Oracle Model, where only one non-programmable random oracle is available. The security of our proposal is based on two well-established post-quantum hardness assumptions from coding theory: The Syndrome Decoding and the Goppa Distinguisher. We prove that our proposal is perfectly hiding and computationally binding.
Multiple Partial Empirical Kernel Learning with Instance Weighting and Boundary Fitting

By dividing the original data set into several sub-sets, Multiple Partial Empirical Kernel Learning (MPEKL) constructs multiple kernel matrixes corresponding to the sub-sets, and these kernel matrixes are decomposed to provide the explicit kernel functions. Then, the instances in the original data set are mapped into multiple kernel spaces, which provide better performance than single kernel space. It is known that the instances in different locations and distributions behave differently. Therefore, this paper defines the weight of instance in accordance with the location and distribution of the instances. According to the location, the instances can be categorized into intrinsic instances, boundary instances and noise instances. Generally, the boundary instances, as well as the minority instances in the imbalanced data set, are assigned high weight. Meanwhile, a regularization term, which regulates the classification hyperplane to fit the distribution trend of the class boundary, is constructed by the boundary instances. Then, the weight of instance and the regularization term are introduced into MPEKL to form an algorithm named Multiple Partial Empirical Kernel Learning with Instance Weighting and Boundary Fitting (IBMPEKL). Experiments demonstrate the good performance of IBMPEKL and validate the effectiveness of the instance weighting and boundary fitting.
Quantum Network Coding For Remote State Preparation of Multi-qudit States

the purpose of this paper is to promote a butterfly quantum network coding scheme for remote state preparation of multi-qudit states. Firstly, we perform channel preparation and amplitude modulation; secondly, each sender simultaneously performs projection measurement; thirdly, both senders send measurement results as precoding information and auxiliary information to the intermediate node and the neighboring receivers respectively; fourthly, the first intermediate node performs encoding operation and sends the encoded information to both receivers via next intermediate node. Finally, by decoding the coded information with auxiliary information, two receivers perform corresponding unitary operations to recover the target multi-qudit quantum states. Compared with other schemes, our scheme can enhance the throughput and transmission efficiency of communication network for remote state preparation of multi-qudit states.
Leveraging Pre-trained CNN Models for Skeleton-Based Action Recognition

Skeleton-based human action recognition has recently drawn increasing attention thanks to the availability of low-cost motion capture devices, and accessibility of large-scale 3D skeleton datasets. One of the key challenges in action recognition lies in the high dimensionality of the captured data. In recent works, researchers draw inspiration from the success of deep learning in computer vision in order to improve the performances of action recognition systems. Unfortunately, most of these studies do not leverage different available deep architectures but develop new architectures. Most of the available architecture achieve very high accuracy in different image classification problems. In this paper, we use these architectures that are already pre-trained on other image classification tasks. Skeleton sequences are first transformed into image-like data representation. The resulting images are used to train different state-of-the-art CNN architectures following different training procedures. The experimental results obtained on the popular NTU RGB+D dataset, are very promising and outperform most of the state-of-the-art results.
PENULISAN ARTIKEL ILMIAH UNTUK GURU SEKOLAH DASAR SE-KABUPATEN BANGKA

Writing article is an absolute requirement for teachers who will rise in rank and certain classes. In the fact, there are so many teachers to retirees only in the IV / a. The main problem is about credit numbers from sub-elements of scientific publications and / or elements of innovative work. This workshop aims to provide an understanding for elementary school teachers in writing article. The method used is a workshop that was held for two days with a total of 32 hours of learning hours. The results obtained from this workshop were an increase in teacher understanding of writing article and producing article papers from the teacher.
ANALYSIS OF INTERNAL CONTROL OF INVENTORY ACCOUNTING INFORMATION SYSTEM AT PT. ANDRE LAURENT

Accounting information system (AIS) is used to assist data processing in a company with the aim to optimize the performance of employees based on the duties and responsibilities of each employee. PT Andre Laurent currently still has obstacles in recording its inventory that causing the difference of inventory stock. This study aims to analyze the procedures of accounting information systems and implementation of internal controls in the company and to design proposals and also to implement a good accounting information system for the company. The research method used in this research is qualitative research method, data collection method by interview, observation or direct observation to the company, documentation and questionnaire is distributed to users of accounting information system proposed based on the discussion. The results of the research are such as the application of FIFO (First-In First-Out) method to inventory recording, improvement of procurement and expenditure procedures, and implementation of Accurate 5 Education Software that will optimize the company's operational activities.
No escape from the island: On extraction from complement wh-clauses in English

In theoretical syntax, English complement wh-clause are considered syntactic islands which block extraction in an asymmetric way: Argument extraction is more acceptable than adjunct extraction.Though this pattern is often assumed to be universal, studies have shown that Danish (and other Mainland Scandinavian languages) may be exceptions.It has also been argued that the patterns of (un)acceptability are biased by expert intuitions.We present data from 100 native speakers of English which confi rms (i) that English complement wh-clauses are islands, (ii) that there is a (subtle) argumentadjunct asymmetry, and (iii) that this acceptability pattern is not due to participant bias.Together with earlier fi ndings on Danish, these results are compatible with an island account that relies on parametric variation in the possibility of CP-recursion.
Mastering Written Scientific Genres As A Prerequisite For Effective Teaching Academic Writing

The article is devoted to the issue of developing writing skills in academic and scientific communication spheres at university levels, both graduate and postgraduate. To teach academic/scientific writing effectively the authors propose to consider forming and developing genre competence of graduate and postgraduate students in scientific communication sphere to be an important component of the methodology aimed at teaching written scientific genres and their varieties. Based on cognitive, discursive and genre approaches the notion and structural components of genre competence in scientific communication sphere are developed. The use of the intergenre model of scientific text is believed to be an effective learning tool for mastering written scientific genres. The integrated course designed in accordance with the principle of comparative learning and teaching, the principle of bilingual learning, the principle of interdisciplinary and integrated learning and teaching, and aimed at developing writing skills in Russian and English is considered as a cornerstone component of effective teaching academic/scientific writing at a university.
On the predictability of high and low prices: The case of Bitcoin

Bitcoin has attracted the attention of investors lately due to its significant market capitalization and high volatility. This work considers the modeling and forecasting of daily high and low Bitcoin prices using a fractionally cointegrated vector autoregressive (FCVAR) model. As a flexible framework, FCVAR is able to account for two fundamental patterns of high and low financial prices: their cointegrating relationship and the long memory of their difference (i.e., the range), which is a measure of realized volatility. The analysis comprises the period from January 2012 to February 2018. Empirical findings indicate a significant cointegration relationship between daily high and low Bitcoin prices, which are integrated on an order close to the unity, and the evidence of long memory for the range. Results also indicate that high and low Bitcoin prices are predictable, and the fractionally cointegrated approach appears as a potential forecasting tool forcryptocurrencies market practitioners.
Reassembly of Fractured Object Using Fragment Topology

This work presents our results on reassembly of broken objects using a newly developed fragment topology and feature extraction methodology.The reassembly of broken objects is a common problem in different domains including computer-aided bone fracture reduction [1] and reassembly of broken artefacts [2].The new fragment topology combines information from intact and fractured region boundaries to reduce possible correspondences between the fragments and optimise our iterative matching process.Experiments performed on different multifragment objects show that the proposed topology can be effectively applied, completing the process in a small number of iterations and with average alignment error 0.12mm.
Rancangan Bigbook Bilingual dalam Konteks Budaya Jawa Barat

This research is the background of the phenomenon of cultural crisis that should be resolved Cultural Preservation in the form of efforts to protect, develop and utilize a dynamic culture. It is not only the global problem that triggers cultural amnesia but individual self-factors that should be aware of and action among elementary school children that is happy with foreign cultures such as cinema, reading books, food and appearance styles have become the development of the current modern era. This study aims to describe the development, implementation and identify the difficulties faced by students in the development of Big Book as a tool for developing media literacy for elementary school children in the cultural context of West Java. Regional coverage in West Java which is the subject of analysis is Bogor, Sukabumi, Cianjur, Bandung, Karawang, Subang, Sumedang, Garut, Tasikmalaya, Kuningan and Cirebon.
Sentence Simplification Based on Multi-Stage Encoder Model

Sentence simplification aims to simplify a complex sentences while retaining its main idea. It is one of the most important tasks in natural language processing. Recent works addressed the task with sequence-to-sequence(Seq2seq) model. However, these conventional Seq2seq models usually based on a single-stage encoder, which only read the source complex sentence once, as a result, it was hard to extract the representational features of the source sentence precisely. To resolve the problem, we proposed a multi-stage encoder based Seq2seq model for sentence simplification. Specificly, there were three stages in the encoder of proposed model, namely N-gram reading stage, glance-over stage and final encoding stage. The N-gram reading stage catched N-gram feature embedding for other stage and the glance-over stage extracted local and global information about the source sentence. The final encoding stage took advantage of the information extracted by the former two stage to encode source sentence better. Then, it introduced a novel attention connection method which could help the decoder to make full use of the information of encoder. Experiments on three public datasets demonstrated the proposed model that outperforms state-of-the-art baseline simplification systems.
Penerapan Model Pembelajaran Think Pair Share Untuk Meningkatkan Hasil Belajar Matematika Siswa Kelas 3 SDN 009 Rambah Samo

This research background was the low learning outcomes of students in grade 3 at SDN 009 Rambah Samo. The purpose of this study is to improve student mathematics learning outcomes through the application of Think Pair Share (TPS) learning. The type of research conducted is classroom action research. The study implemented in two cycles, with three meetings in each period. Classroom action research procedures carried out include planning, implementing, observing, and reflecting. The instrument used was a test of learning outcomes and an observation sheet of teacher and student activities. The results showed that the average student learning outcomes increased from 52.95 in the initial data to 69.86 in the first cycle with a percentage increase of 31.94% and 84.17 in the second cycle with an increase from cycle 1 of 20, 7%. Individual completeness obtained from the initial data up to cycle 2 was 43.47%, 65.21%, and 78.26%, respectively. So it can be concluded that the application of the TPS learning model can improve mathematics learning outcomes of students in grade 3 at SDN 009 Rambah Samo.