Reviewerly/exHarmony huggingface dataset:
韓國古代漢字石刻同版異文研究—以〈真鑑禪師碑〉爲例

The history of the spread and development of Chinese characters in Korea is very long. Chinese characters were introduced into the Korean peninsula as early as in the period from the end of the Han Dynasty to the Three Kingdoms (A.D. 3C). Until the end of the 19th century, Chinese characters were the official writing system in Korea. The Korean peninsula has a history of using Chinese characters for over 1,500 years. It has an incomparably rich collection of ancient texts and documents written with Chinese characters. Of them are a considerable number of stone carvings that authentically recorded the profound literacy of the Korean people in the Middle Ages as far as the culture of Chinese characters is concerned. At the same time, these stone carvings are also of valuable reference for the developmental transformations and the configurational patterns since the formation of the clerical script, through the Wei-Jin period, the Sui and the Tan dynasties, and all the way through modern times. The present study takes as an example of the stone tablet of Master Chinkam Sŏnsa of the Silla era collected in Korean Grand Compendium of Ancient Inscriptions published by the Korean Studies Institute. I specifically summarized and analyzed the alternative script of ancient Chinese characters in the Korean stone carvings. I found out that there are characters written with different strokes or in a different internal structure, those which have been simplified, those which some parts have been added to or subtracted from, and those with a different position. Then I explored the formation and the developmental trajectory of individual variant forms of a Chinese character. Thus, I grasped the reasons for the formation of the variant Chinese characters in Korean ancient stone carvings as well as their writing characteristics.
Detecting Synonymous Properties by Shared Data-Driven Definitions

Knowledge graphs have become an essential source of entity-centric information for modern applications. Today’s KGs have reached a size of billions of RDF triples extracted from a variety of sources, including structured sources and text. While this definitely improves completeness, the inherent variety of sources leads to severe heterogeneity, negatively affecting data quality by introducing duplicate information. We present a novel technique for detecting synonymous properties in large knowledge graphs by mining interpretable definitions of properties using association rule mining. Relying on such shared definitions, our technique is able to mine even synonym rules that have only little support in the data. In particular, our extensive experiments on DBpedia and Wikidata show that our rule-based approach can outperform state-of-the-art knowledge graph embedding techniques, while offering good interpretability through shared logical rules.
Create Your Own LoL Facebook Cover Style

Do you know that you can create your own LoL Facebook cover style from this page? If you are here for the first time, we are glad that you have found ...
PERBANDINGAN ALGORITMA PADA VIRTUAL PRIVATE NETWORK IPSEC TERHADAP KECEPATAN DATA TRANSFER

VPNs can slow down internet connections because data must travel through the VPN network and encrypt and decrypt data that requires more time.Humans need a solution to make it easier in terms of obtaining the speed of time, security, reduce costs, and get the ease of communication without thinking about distant places.Several algorithms can be applied to VPN technology including AES, DES, 3DES, RC4, Blowfish and IDEA, but it is not yet known from these algorithms that the most optimal performance in terms of data transfer speeds.This research will compare algorithms on IPSec Virtual Private Network (VPN) technology to data transfer speeds using cisco packet tracer tools and only compare DES, 3DES, AES 128, AES 192 and AES 256 algorithms.The contribution of this study is to show that the 3DES algorithm as the most optimal algorithm in terms of data transfer speeds on files measuring 5571584 bytes with a ratio of 13.69% slower, files measuring 33591768 bytes with a ratio of 14.97% slower and on files measuring 16599160 bytes with a ratio of 8.36% slower on download process.While in the upload process, the file size is 5571584 bytes with a ratio of 11.17% slower, the file size is 33591768 bytes with a ratio of 11.66% slower and the file size is 16599160 bytes with a ratio of 9.05% slower.
IMPLEMENTING COMMUNICATIVE APPROACH IN TEACHING SPEAKING

This study focused on investigating types of teaching techniques used by English lecturers in teaching speaking and how the implementation types of teaching techniques in teaching speaking under the guidelines of three questions: (1) What types of teaching techniques are used by the English lecturers in teaching speaking? (2)  Why do they apply techniques in teaching learning process of speaking? (3) How are the implementation types ofteaching techniques in the teaching learning process of speaking?This research was conducted using descriptive qualitative research. Descriptive qualitative method was chosen because this research dealt with words not number. Besides, the data were described descriptively in the form of paragraph. The data analysis revealed some findings covering the formulation of the research questions.The result of the study showed thatspeech technique was performed by lecturers in teaching speaking that students were really enthusiast during teaching and learning process through speech. Speech also can increase students’ speaking skill. The result of the study could be used to increase teaching and learning process of speaking in order to improve speaking skills.
Line and Path Detection

An image analysis problem can often be described as a task of recognizing patterns in images. Example patterns are straight lines and curve-like structures. The Hough transform is a highly effective algorithm for detecting straight lines in images and is the topic of the first part of the chapter. In the second part, a method for tracing paths in images is presented. The algorithm is very fast and is based on the algorithmic approach called dynamic programming.
A Study on Enhancement to the Overall Management of the Special Cases of State Property

Although the system for the regulation of special cases of state property(“Special Cases”) has been in effect since 2011 to limit the lax management of Special Cases, and the reckless formulation of provisions for Special Cases, reduction in national fiscal revenue due to the operation of Special Cases has been increasing, and amounts to around USD 835 million. This shows it is difficult for the Ministry of Economy and Finance (“MOEF”) to control overall Special Cases under the current system. In this regard, this study examines ways to strengthen MOEF’s function to promote the rational operation of Special Cases. First, the operational method of the Comprehensive Plan for Special Cases should be changed from the current system of gathering plans from central government agencies to the system of approving property statements. Second, the reduction or exemption of usage fees or the transfer of state property that occur under the State Property Act should also be controlled by the system of Special Cases. Third, it is necessary to strengthen institutional foundation to ensure efficient inspection and evaluation of Special Cases. Lastly, legal grounds for the priority of the Act on Special Cases of State Property over other acts should be prepared.
Aesthetic education of preschool children in the new socio-cultural conditions

In this article, the researchers draw the attention of the professional pedagogical community in the field of preschool education to the new meaning of the aesthetic education of preschool children in the sociocultural situation of the 21st century. The tasks of the education system as a social institution of society are revealed. The history of the concept of sustainable development as applied to the theory and practice of preschool education is presented briefly. The role of aesthetic education in the holistic development of the personality of a child of preschool age is defined. The analysis of the Federal state educational standard of preschool education in terms of the aesthetic education of preschool children is presented. The mission of an integrated approach in the aestheticization of preschool childhood is formed. The methods and scientific and pedagogical conditions of the aesthetic education of children of preschool age in a new socio-cultural reality are reflected.
Effects of Language Relatedness for Cross-lingual Transfer Learning in Character-Based Language Models

Character-based Neural Network Language Models (NNLM) have the advantage of smaller vocabulary and thus faster training times in comparison to NNLMs based on multi-character units. However, in low-resource scenarios, both the character and multi-character NNLMs suffer from data sparsity. In such scenarios, cross-lingual transfer has improved multi-character NNLM performance by allowing information transfer from a source to the target language. In the same vein, we propose to use cross-lingual transfer for character NNLMs applied to low-resource Automatic Speech Recognition (ASR). However, applying cross-lingual transfer to character NNLMs is not as straightforward. We observe that relatedness of the source language plays an important role in cross-lingual pretraining of character NNLMs. We evaluate this aspect on ASR tasks for two target languages: Finnish (with English and Estonian as source) and Swedish (with Danish, Norwegian, and English as source). Prior work has observed no difference between using the related or unrelated language for multi-character NNLMs. We, however, show that for character-based NNLMs, only pretraining with a related language improves the ASR performance, and using an unrelated language may deteriorate it. We also observe that the benefits are larger when there is much lesser target data than source data.
Perceptions of Early Childhood Student Teachers towards the Online Tutorial Courses at Universitas Terbuka (UT)

This article discusses how UniversitasTerbuka (UT) has helped to change the way of teaching, especially early childhood education (ECE) student teachers learn in Indonesia, from being the passive students who were listening to the teacher in the class to be those who can learn actively and independently both face to face and online.The research was conducted in 2018 and 2019 by sending some open questions online and by interviewing some early childhood education (ECE) student teachers.The study also used the data from 2013 as comparison data.The data have been analyzed descriptively.The results showed that it is proven that UT has a role as an agent of change for the early childhood education (ECE) student teachers in Indonesia.By studying at UT, the ECE student teachers learn to explore actively and independently.Moreover, UT (Universitas Terbuka) has helped them to experience how to learn online.By participating in an online tutorial at UT, the ECE student teachers learn to use internet for learning, such as reading the learning materials online, by expressing their knowledge and posting their questions online.Thus, in UT, they learn to know and to learn how to be professional ECE student teachers.
Analysis of Curriculum Implementation in Innovative Schools: Focusing on the Curriculum, Instruction, and Evaluation

The aim of this study is to analyze how the curriculum is implemented in innovative elementary, middle, and high schools and to suggest the direction of its implementation for innovative schools. A nationwide survey was conducted focusing on the curriculum, instruction, and evaluation, and a total of 1,992 responses from teachers working at innovative schools were collected and analyzed. The results indicate that innovative schools followed the direction pursued by the school curriculum well; furthermore, the opinions of members were reflected in the school curriculum in a culture in which teachers, parents, and students communicate and negotiate. In addition, in the innovative school, the curriculum was organized and implemented in connection with the local community, and projects with integrated subjects, classes with active student participation and interaction, and learning- and growth-oriented evaluation were actively conducted. However, the aspects of sharing, participation, communication, and cooperation and the implementation of learner-centered education were better realized at the lower school levels. Finally, considering the results of this study, we suggest five directions for implementation in the curriculum of innovative schools.
Information System Cyber Threats and Telecommunications Engineering Courses

The article discusses the issue of training of telecommunications engineers. The architecture of telecommunications solutions is changing very quickly. Obviously, training programmes must also change. Cybersecurity issues are one of the main drivers of change in telecommunications solutions and, therefore, training programmes. They have become the main issues in all processes related to digital transformation. At the same time, it is clear that the development of education in telecommunications clearly lags behind modern requirements. Such issues come to the fore in relation to the development of digital economy programmes. Cyber security issues for military telecommunications solutions are discussed separately.
Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features

With the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting user privacy. In particular, a synthetic trace that preserves a feature specific to a cluster of users (e.g., those who commute by train, those who go shopping) is important for various geo-data analysis tasks and for providing a synthetic location dataset. Although location synthesizers have been widely studied, existing synthesizers do not provide sufficient utility, privacy, or scalability, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer called PPMTF (Privacy-Preserving Multiple Tensor Factorization). We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We factorize these two tensors simultaneously via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors, and perform a plausible deniability test for a synthetic trace. We comprehensively evaluate PPMTF using two datasets. Our experimental results show that PPMTF preserves various statistical features including cluster-specific features, protects user privacy, and synthesizes large-scale location traces in practical time. PPMTF also significantly outperforms the state-of-the-art methods in terms of utility and scalability at the same level of privacy.
6. Early Musical Education as a Premise for the Development of the Child’s Upright Personality

Abstract A person upright development is essentially connected to communication, and music is the language with the highest degree of communication, is the purest and deepest language, able to express the hidden, the essential, the spiritual. Musical education, achieved in all forms, contexts, periods, etc. under the mark of the upright development of human personality, means “wisdom, balance, love, inner light, harmony, “feeling”, etc.”. [I. Gagim]. Childhood, for this purpose, remains the fundamental stage for the subsequent development and initiation of the child in the “real” world, constituting at the same time, the foundation for his/her human “ becoming”.
Adversarial Metric Learning with Naive Similarity Discriminator

Metric learning aims to generate similarity-preserved low dimensional feature vectors from input images. Most existing supervised deep metric learning methods usually define a carefully-designed loss function to make a constraint on relative position between samples in projected lower dimensional space. In this paper, we propose a novel architecture called Naive Similarity Discriminator (NSD) to learn the distribution of easy samples and predict their probability of being similar. Our purpose lies on encouraging generator network to generate vectors in fitting positions whose similarity can be distinguished by our discriminator. Adequate comparison experiments was performed to demonstrate the ability of our proposed model on retrieval and clustering tasks, with precision within specific radius, normalized mutual information and F1 score as evaluation metrics.
Research of Computational Thinking based on Analyzed in Each Major Learner

The rapid development of a software-core society emphasizes the importance of software competence as a basic condition for all academic disciplines. The purpose of this study is to investigate the difference of perceptions among students of basic software education which is currently being conducted in university. The results of applying the nine core elements of Computational Thinking for Problem Solving to the learners of the each majors are as follows. In humanities, learners mainly applied the elements of Data Collection, Problem Decomposition and Automation. On the other hand, natural science department learners mainly applied the elements of Data Analysis, Algorithm and Automation. In addition, arts learners mainly applied elements of Data Representation, Abstraction, and Automation. To apply Computational Thinking to the development of software, humanities learners mainly applied elements of Data Collection, Algorithm, Automation. On the other hand, natural science department learners mainly applied the elements of Data Analysis, Algorithm and Automation. In addition, arts learners mainly applied elements of Data Representation, Abstraction, and Automation. Based on the results of this study, it is expected that the educational effectiveness of the learner will be maximized by including the learner analysis with each majors in the design of the basic software curriculum that each university is conducting.
A prospective study to determine the accuracy of triple test score in breast lump

Background: Breast cancer is the most common cancer in women worldwide. The commonest mode of presentation of diseases of the breast is lump. It is the most common site-specific cancer in women. Triple test score which includes clinical breast examination, mammogram, FNAC score. A simple non-invasive but reliable test can make a huge difference in management between benign and malignant lumps. Aims and objectives of the study were to differentiate between benign and malignant lesions based on TTS and correlated the accuracy of triple test score with histopathology report, and plan the management accordingly.Methods: A prospective study was carried out in which TTS was calculated by summation of individuals’ scores of all three components and compare with histopathology report.Results: In present study of 74 patients (age group of 30 to 65 years), based on TT score 52 benign, 12 malignant, and 10 suspicious (neither benign nor malignant). But the final histopathological result showed 59 as benign and 15 as malignant, which is in concordant with TTS, which shows the accuracy of up to 100%. Out of 10 (suspicious) which were dis-concordant, 7 are benign and 3 are malignant on histopathology.Conclusions: By use of the triple test score and its interpretation, definitive treatment can be initiated, which would reduce the need for unnecessary biopsy and its ability to predict benign lump, can avoid major surgery.
How To Restore An iPhone From A Backup On iCloud

On this page, I will list an easy step by step guide you can follow on how to restore an iPhone from a backup on iCloud and iTunes. How To Restore An ...
Unsupervised Feature Selection via Multi-step Markov Transition Probability

Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability. Many methods have been proposed and achieved good results, in which the relationships between adjacent data points are mainly concerned. But the possible associations between data pairs that are may not adjacent are always neglected. Different from previous methods, we propose a novel and very simple approach for unsupervised feature selection, named MMFS (Multi-step Markov transition probability for Feature Selection). The idea is using multi-step Markov transition probability to describe the relation between any data pair. Two ways from the positive and negative viewpoints are employed respectively to keep the data structure after feature selection. From the positive viewpoint, the maximum transition probability that can be reached in a certain number of steps is used to describe the relation between two points. Then, the features which can keep the compact data structure are selected. From the viewpoint of negative, the minimum transition probability that can be reached in a certain number of steps is used to describe the relation between two points. On the contrary, the features that least maintain the loose data structure are selected. And the two ways can also be combined. Thus three algorithms are proposed. Our main contributions are a novel feature section approach which uses multi-step transition probability to characterize the data structure, and three algorithms proposed from the positive and negative aspects for keeping data structure. The performance of our approach is compared with the state-of-the-art methods on eight real-world data sets, and the experimental results show that the proposed MMFS is effective in unsupervised feature selection.
Application of virtual reality technology in distributed measurement training system

With the rapid development of virtual reality technology, virtual reality technology has been widely used in many fields, especially in the field of education and training. Traditional measurement training is subject to economics, repeatability, safety, etc. The independent practice and teaching interaction in the training process are not ideal. This paper proposes the application concept of integrating virtual reality technology into the measurement training system. The trainee can interact with the virtual reality environment in real time, effectively avoiding the waste of measurement resources and improving the safety and repeatability of measurement training. At the same time, the system can provide the super immersion feeling that the traditional training mode can not provide, so that the trainees can feel the real measurement environment, improve the field measurement support ability, and effectively enhance the training effect. For the multi-party joint guarantee measurement task, the distributed characteristics of the system can be utilized, so that the metering personnel can still perform joint training in the same training scenario in different regions, and realize systematic joint training and joint training.