Reviewerly/exHarmony huggingface dataset:
LatinISE corpus (version 4)

The LatinISE corpus is a text corpus collected from the LacusCurtius, Intratext and Musisque Deoque websites. Corpus texts have rich metadata containing information as genre, title, century or specific date.This Latin corpus was built by Barbara McGillivray.In the version 4 of the corpus the high frequency lemmas have been manually corrected and sentence boundaries have been added.
Semantics of negative sequential patterns

In the field of pattern mining, a negative sequential pattern is specified by means of a sequence consisting of events to occur and of other events, called negative events, to be absent. For instance, containment of the pattern a ¬b c arises with an occurrence of a and a subsequent occurrence of c but no occurrence of b in between. This article is to shed light on the ambiguity of such a seemingly intuitive notation and we identify eight possible semantics for the containment relation between a pattern and a sequence. These semantics are illustrated and formally studied, in particular we propose dominance and equivalence relations between them. Also we prove that support is anti-monotonic for some of these semantics. Some of the results are discussed with the aim of developing algorithms to extract efficiently frequent negative patterns.
A Study on the Content of the Official Chinese Language School Textbook 〈Jungjujeongeum〉 in the Korean Empire Period

『Jungjujeongeum』 is a manuscript that records the Chinese learning contents of the official Chinese language school opened in 1897. There is no data to confirm the Chinese education of this period so far. However, 『Jungjujeongeum』 was discovered and some of the aspects of Chinese education at that time were possible. Through the records of the scribe, it can be seen that the Chinese curriculum at that time was very important to pronunciation education enough to distinguish between pronunciation learning and tone learning. Each pronunciation was first practiced in one syllable in detail by tone, and the application was practiced through a short sentence or proverb containing the pronunciation. Although the contents of the conversation are not included in much, it cannot be examined in detail, but it is presumed that it was influenced by Chinese textbooks published in Japan. With the discovery of this data, we hope that it will be an opportunity to review the changed aspects of modern foreign language education in Korea at the end of the 19th century.
A Comparative Analysis on Nastaliq Style Urdu Character Recognition

Optical Character Recognition (OCR) has emerged as an interesting research field. Lot of work has been declared in Urdu script based on various approaches and diverse methodologies have been put forward on Nastaliq font style to get the desired output. The paper presents a survey on different techniques of OCR and ends up with the comparative analysis of Urdu character recognition based on accuracy and other performance parameters. This exploration directly implies that the nonpresence of Urdu OCR has restricted the idea on advanced Urdu library and thus, drives a pathway for enormous research in this field.
Efficient Transformation of Protein Sequence Databases to Columnar Index Schema.

Mass spectrometry is used to sequence proteins and extract bio-markers of biological environments. These bio-markers can be used to diagnose thousands of diseases and optimize biological environments such as bio-gas plants. Indexing of the protein sequence data allows to streamline the experiments and speed up the analysis. In our work, we present a schema for distributed column-based database management systems using a column-oriented index to store sequence data. This leads to the problem, how to transform the protein sequence data from the standard format to the new schema. We analyze four different methods of transformation and evaluate those four different methods. The results show that our proposed extended radix tree has the best performance regarding memory consumption and calculation time. Hence, the radix tree is proved to be a suitable data structure for the transformation of protein sequences into the indexed schema.
A Study of Mission Theological Consideration and Application of GOC from UK and GOCN from North America to Churches in Korea

This article is a study about missional church movement in England and North America. In particular, the study is focused on the mission theology and the ecclesiastical theory of GOC movement in the United Kingdom and GOCN movement in North America. Currently, the Korean church faces serious problems internally and externally. The church is drifting and losing its resilience. Big and small problems in church include: breakdown of church community, immorality of pastors, unbiblical charism, materialism, success-oriented faith. These intrude and strand the church. The church is missing the purpose of God’s establishment of church in the world. With missional movements of the West, we have analyzed problems of Korean churches and have been looking for a new direction and goal for Korean church and culture.
RQE: Rule-Driven Query Expansion to Solve Empty Answers in SPARQL

A branch of question answering approaches translates natural language questions to SPARQL queries. The empty answer problem exists even when we have properly-translated ones, due to the heterogeneity and incompleteness of knowledge graphs. Existing methods use similarities, ontologies or embeddings to relax failed queries and obtain approximate answers, but they may lose efficacy in approximating simple queries with only one or two constraints because of their low accuracy and suitability for over-constrained ones. In this paper, we propose a rule-driven query expansion approach to expand failed queries for obtaining more accurate approximate answers. Specifically, we first automatically build high-quality rule sets for predicates in failed queries with rule learning techniques. Then, we use the learned rules to expand failed queries to get approximate answers and explain the reasons why we choose these answers. We develop two datasets to evaluate the effectiveness and efficiency of our approach and the results show that our approach achieves better results than several approaches based on similarities, ontologies and embeddings in approximating simple queries.
Justification of the professional image for the development of the educator's professional image

The paper theoretically substantiates the pedagogical conditions for the development of the educator's professional image as a component of the entrepreneurship education model. The conducted analysis of the scientists' approaches to determining the components of the educator's professional image demonstrated that the structure of this formation is multicomponent. The following components of the educators professional image were identified during the research: appearance (clothes, shoes, accessories, hairstyle, makeup, gait, manners, etiquette); internal image (professional knowledge, skills and abilities, sphere of consciousness, value system, self-concept, reflection, intuition, health, critical thinking, personal qualities: responsibility, emotional stability, erudition, kindness, empathy); communicative component (verbal and non-verbal features of communication), as well as the possibility of their development in the system of postgraduate pedagogical education.
TV: Trade Visualizer An Educational Online Tool for Learning Trade

“How Trade is carried out among countries of the world” a question that is often discussed in research community. Many efforts have been done in past to explore trade, however we argue that our online educational tool has some new and innovative ways to explore trade. Geographic web servers, online web applications and certain wikis display massive sets of data, but usually in the form of tables or textual data such as in CIA World Factbook, Wikipedia etc. We still cannot overlook the significance of tables or textual format as they are the elementary form of data representation from ages. However, upcoming visualization libraries like D3 and emerging trends in map visualization allow us to visualize this tabular data in an evocative way on maps. This is the motivation behind this research paper and online tool. Based on the data taken from Factbook, Wikipedia our tool provides interactive trade visualizations for countries of the world, more specifically for 193 UN countries. These visualizations on one hand provide a summary of facts using maps whereas on other hand they provide an in depth knowledge about exports and imports among countries of the world. We claim that our efforts are unique in many respects and our ideas are worth for research community that are interested in different ways of exploring knowledge and data visualization. More precisely our contributions are: i) Meaningful arrangement of countries on web browser that facilitates users to visualize all countries (193 UN countries) simultaneously without scrolling horizontally or vertically. ii) Visualizing export partners of countries using lines and curves that originate from country of interest towards its export partners residing in the same and in different continents iii) Visualizing ranks of countries for more specific information using animations and physical ordering of countries iv) Visualizing percentage of trade among different partners of countries. v) An interactive lab environment that allows user to select a trade item (a commodity such as carpet, fish, agricultural products, alloys etc) and countries are highlighted that export these commodities on the map. vi) A visual comparison illustrating the difference between import and export partners and identification of countries that are both export and import partners using different visual heuristics. We have developed 3D visualizations representing the globe and map views which allow users to explore trade between countries. These visualizations represent numerous ways of selecting trade partners.
Object Tracking Based on Deep CNN Features Together with Color Features and Sparse Representation

To acquire the better object tracking performance in this paper, the object of interest is modeled by its RGB-color histogram feature together with deep convolutional neural network feature (deep CNN feature) and sparse representation. Because the information of the objects of interest cannot be obtained ahead in practical use, the deep CNN features should be abstracted through the pre-trained VGG network. Then the deep feature and RGB-color histogram are combined to model the object in adaptive mode. And then sparse representation is used to express the model. Above all, an adaptive particle filter algorithm based on deep CNN feature together with RGB-color feature and sparse representation is proposed to track the object of interest. Extensive experiment results demonstrate the effectiveness of our proposed method under the serious object occlusions and appearance changes.
A Trust-Based Security Research Method for Internet of Things Terminal

The Internet of things has a broad application prospect, but under the environment of the Internet of things, terminals are facing extremely serious security threats. The existing security research focuses on authentication and encryption and other technologies, and the lightweight embedded equipment provides the possibility for the hardware security enhancement of the Internet of things terminal. Therefore, this paper introduces the basic concepts of trust and trustworthiness into the Internet of things terminal security improvement. By adding the trusted module to the Internet of things terminal equipment, it enhances the security of the Internet of things terminal from the perspective of active defense and trustworthiness, and obtains good results in practical application.
Attention-Based Direct Interaction Model for Knowledge Graph Embedding

Knowledge graph embedding aims at learning low-dimensional representations for entities and relations in knowledge graph. Previous knowledge graph embedding methods usually assign a score to each triple in order to measure the plausibility of it. Despite of the effectiveness of these models, they ignore the fine-grained (matching signals between entities and relations) clues since their scores are mainly obtained by manipulating the triple as a whole. To address this problem, we instead propose a model which firstly produces diverse features of entity and relation by multi-head attention and then introduces the interaction mechanism to incorporate matching signals between entities and relations. Experiments show that our model achieves better link prediction performance than multiple strong baselines on two benchmark datasets WN18RR and FB15k-237.
Comparative Analysis of Apache Spark and Hadoop MapReduce Using Various Parameters and Execution Time

Due to rapid growth in Information technology there is a lot of advancement in Electronics and communication. Every hour lot of data with various medium is getting generated which is referred as big data. Big Data and Hadoop are the trending terms nowadays. Storage and analysis of such a large data is becoming one of the challenges for computer science and Information Technology devotee throughout the world in the most recent couple of the years. As Apache Spark and Hadoop are the frameworks used for analyzing big data, our paper discusses a comparison of both the frame works by choosing different sizes of datasets and in terms of time comparison also. This comparison is made using word count algorithm. Although both the resources are relayed on an idea of significantly varying Big Data performance. This paper shows an analysis on both frameworks for word count algorithm over Hadoop MapReduce and Apache spark environment
An Automated Fact Checking System Using Deep Learning Through Word Embedding

The increasing concern with false information has stimulated research in joint Fact Extraction and VERification (FEVER). Now we propose a system by deep learning which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia. In this paper, we examine how to use deep learning method to improve the performance of the automatic fact verification system. Firstly, the inverted index of the knowledge base is established by using a Python package named Whoosh. Secondly, the claim is regularized by the Named Entity Recognition (NER) tool, and the most relevant documents are filtered based on the relevance ranking algorithm. Thirdly, top 20 relevant sentences for each claim are filtered by word embeddings. Finally, the effectiveness of each sentence and the label of claim is judged based on the two-level pre-training model. Our approach achieved a 0.89 document.
Blockchain : Comparative Analysis of Public and Private networks

Blockchain technologies have grown swiftly in recent years, primarily due to the advent of Bitcoin. Blockchain is a consensus of data structures or blocks programmed for cryptographically storing organized data, which is spread across nodes, so that various operations can be executed on it. Originally blockchain was designed for storing digital coins as system states and now has grown beyond crypto-currencies to support user-defined decentralized autonomous applications or smart contracts with Ethereum. Growing interest from the industry and wide range of applications has triggered development of new blockchain platforms. As the technology is progressing quickly, it is necessary and challenging to have a more profound perspective of what the core technology platforms have to provide in order to establish which blockchain implementation should be leveraged for a particular application. We therefore grasp the key aspects of blockchain and compare how the current blockchain implementations are different from each other, both qualitatively and quantitatively in terms of design architecture, codebase, consensus algorithms and performance. Drawing from the comparison we identify the current challenges as well as performance issues in blockchain adoption, thus suggesting possible solutions and future implementations to improve performance of blockchain and provide subsequent research directions.
La valutazione dell’adeguatezza funzionale di produzioni orali e scritte in italiano L2 in tipologie di task differenti

The paper discusses the role of variation in L2 learning and, more particularly, the influence of diamesic variation and variation in the communicative context, on oral and written performance in Italian L2. In order to do so, the functional adequacy of oral and written L2 production, in terms of successful taskcompletion is investigated, on the basis of data collected by means of three different tasks. For beingable to assess functional adequacy, a rating scale was developed, inspired by the conversational maxims of Grice. The rating scale of functional adequacy distinguishes four scale dimensions: content, task requirements, comprehensibility, coherence and cohesion. In the paper the applicability of the rating scale will be discussed, in relation to variation in both task modality (oral versus written) and task type (decision making task, narration task, instruction task). The underlying assumption of the present study is thatassessing learner production is not possible without taking into account, next to the linguistic dimensionof L2 performance (caf: complexity, accuracy and fluency), also the functional dimension.
Emerging Scenarios to Enhance Creativity in Smart Cities Through STEAM Education and the Gradual Immersion Method

“Smart cities” are now envisioned as a way to improve civic life in the twenty-first century. In this context, smart cities need innovative industries, and innovative industries require creative people with profiles and skills, capable of solving problems through multidisciplinary means, with an acute vision for identifying opportunities and threats. These trends are driving a boom in creative spaces to learn and produce – things that break with the paradigm of traditional education, even in formal institutions. The projects that occur in these spaces are therefore disruptive: They combine, for example, the tools of digital fabrication and efforts to bring the community together, with the aim of looking for solutions to the immediate environment. The speed with which new technologies emerge stimulates a constant demand for guided learning in future scenarios of smart cities. We used the Gradual Immersion Method (GIM) to promote creativity and collaboration, taking advantage of the benefits of augmented reality in consideration of STEAM. In this chapter, we present two scenarios for applying GIM in a STEAM context, concluding with a proposal for co-creating an interactive and sustainable “smart city” environment.
On Minimal Distance between q-ary Plateaued Functions

It is proved that the Hamming distance between distinct binary s-plateaued functions of n variables is not less than $2^{\frac{s + n - 2}{2}}$ and the Hamming distance between distinct ternary s-plateaued functions of n variables is not less than $3^{\frac{s + n - 1}{2}}$. It is shown that this bound is tight. Constructions and some properties of q-ary plateaued functions are discussed.
Application of list decoding of convolutional codes in steganography

Convolutional codes with Viterbi decoder are widely used in content-adaptive steganography. Although, this approach improves imperceptibility of stego image, it has a substantual drawback. Viterbi algorithm allows only additive metric to be optimized, while steganalysis can exploit this property. In this paper, we introduce the solution of the problem with ML list decoding procedure. We propose to obtain list of possible vectors by additive criterion and then select one of them by steganalysis criterion.
A Study on Adjunct of Serial-Verb Structure

The clause of serial-verb structure is the special sentence type that are consist of two verbs or over two verbs, the structure is very complicated and various, so many problems still are in constant discussion, the adjunct of serial-verb structure is one of them. this paper according to the generation grammar
UBERGP

For successful life human being needs good health but illness is a part of life. The crowd in the hospitals, long waiting of doctor appointments makes the patients more disturb. Sometimes the patient sees the other serious patients in hospitals which makes him mental sick. Also due to busy life schedules, the peoples have not enough time to visit the doctor's clinics for small health problems which may lead to a serious disease. In this regard, the application UberGP is developed to assist the patients to connect with the doctor on phone to phone consultancy or book appointment to come doctor at home. This application has numerous benefits in case of time saving and quick appointment system at home or office. This study provides the detail documentation and usage of the UberGP Application.
Christian Spirit of the March First Independence Movement and Its Implication for the Missionary Tasks of the Korean Church

The aim of this paper is to identify the Christian spirit of the March First Independence Movement and to suggest the missionary tasks of the Korean Church based upon the Christian spirit of the March First Independence Movement. This paper identifies the Christian spirit of the March First Independence Movement as self-reliance and independence, democracy, peace, ecumenical spirit, and interfaith cooperation and solidarity. The implication of the Christian spirit for the missionary tasks of the Korean Church is to reinforce the global south church, to contribute to socio-economic democratization and church democratization as well, to make effort for the peace education, peaceful reunification of Korea, to recover the holistic type of faith, and to support the interfaith cooperation and solidarity.
OBDMR: An Ontology-Based Data Model for Railways

The domain of the railway system is vast and complex since it includes several sub-domains hierarchy in it. These sub-domains include different branches of technology and operational hierarchy. Many types of research are running on and have happened in this vast domain along with different technologies. Among all available technologies ontology is the single one which talks about semantics and thus supports the decision support system. This paper proposes an OBDMR model for railway systems to integrate the information at the knowledge level. The paper has used railML (version 2.2) as a data resource as railML covers all the aspects of the railway system. railML (Railway Mark-up Language) is an open, XML-based data exchange format for data interoperability of railway application. The proposed ontology adds the semantics to the given data and even allows to infer new information from current data which XML cannot do. OBDMR is capable of taking decisions by automated reasoning using software agents. A generic model proposed in this paper satiates the standards and specifications of most countries’ railway systems. A use-case for Indian Railways is discussed with some examples.
The Globalized Library: American Academic Libraries and International Students, Collections, and Practices

"The Globalized Library: American Academic Libraries and International Students, Collections, and Practices." Journal of Access Services, 17(2), pp. 111–112