Reviewerly/exHarmony huggingface dataset:
A Study on U.S. Commercial Public Policy on Automotive and Steel Against Korea

트럼프 행정부의 통상정책은 기존 미국의 통상정책과 달리 한 기업의 대응 수준을 넘어 민관이 협력하여 광범위하게 대응하여야 할 정도로 확대되어 불확실성과 대응범위를 증폭시켰다. 그럼에도 불구하고 무역확장법 232조, USMCA등 국가 기간산업을 뒤흔들 통상이슈에 대한 분석과 대응방안에 대한 연구는 부족한 실정이다. 이에 따라 본 연구는 한국이 처해있는 주요산업의 통상현안 사례를 분석하여 대응방안과 정책적 시사점을 제시하는데 목적이 있다. 본 연구가 제시하는 시사점과 대응방안은 기업·업계·국가 차원에서 대응해야할 정책적 대안이 될 수 있으며 그간 연구되지 않았던 광범위한 통상현안과 주요산업을 연계한 분석으로 추가적인 학문적 연구방안을 제시할 수 있을 것이라 생각한다.Unlike a traditional commercial public policy, the Trump administration has adopted maximum pressure that past the present level of a company and require Public-Private Cooperation. Also, Commercial public policy of Trump administration raising uncertainty and range of responses. Nevertheless, it is true that there are few studies related commercial issues such as Anti- dumping, Section 232 of the Trade Expansion Act and USMCA that shake basic industries. The purpose of this research is that suggest policy implication and possible countermeasure by analysing confronted commercial issues and cases of key industry. The implication of this research are that policy for corporation, industry and national level. Also, this research that able to link extensive commercial issue and key industry.
Interdisciplinary Network Project as a Factor of the Ecologization of Youth Consciousness

Scientific and technological progress, globalization, information technologies advances have shown that the environmental component is an integral part of the development of human society. Modern socioeconomic and technological challenges impose special requirements on the education system and educational outcomes, which should drastically transform the worldview of a young person changing the vector of their value orientations towards ecologization of consciousness and greening of lifestyles and activities. The purpose of this publication is to focus on the opportunities and prospects of interdisciplinary young people’s activities that are implemented as an international networking project on environmental issues. The methodological basis of the study are conceptual provisions of the interdisciplinary approach, ecologization of education, and the project method. The article presents the description and results of the interdisciplinary environmental network project entitled “Ecological Paths Through the Hanseatic League of the New Time” implemented in anticipation of the XXXIX International Hanseatic Days of the New Time, which took place in Pskov in June 2019. One can state that such a practice-oriented approach in teaching has resulted in changing the young people’s perception of regional environmental problems. They have got steadily motivated in taking actions aimed at improving the environmental situation.
Application of Improved Dijkstra Algorithm in Two-dimensional Path Planning Problem

The Dijkstra algorithm is a typical single-source shortest path algorithm for calculating the shortest path from one node to the other in a non-negative weight map, but its use of the roulette method greatly affects the node selection. Speed and efficiency. Therefore, on the basis of ensuring the search accuracy, this paper improves the initial Dijkstra algorithm to improve the efficiency of the algorithm and meet its needs in 2D or 3D path planning. In this paper, MATLAB2018a is used as the experimental platform to simulate the initial Dijkstra algorithm and the improved Dijkstra algorithm. The experimental results show that the improved algorithm greatly reduces the path planning vision and improves the operating efficiency.
Impact of Blockchain Technology on Efficiency of Financial Transactions

Blockchain technology is emerging strong in many areas and is playing a major role in financial transactions, mainly in the banking domain. The study was conducted using primary data by considering the convenient sampling method. The study examined the perceptions on the usage of blockchain technology's implementation with the help of statistical method of discriminant analysis and the results revealed that the perception was higher in case of the attributes such as mitigation of transaction costs and highly secured. The SEM model indicated that smart contracts had a higher impact on the efficiency of financial transactions followed by digital currency. This paper will be useful to the various financial institution regulators, stakeholders, and academicians.
The Coruña corpus tool: Ten years on

In this paper we provide a brief introduction to a new version of the Coruna Corpus Tool. Currently available for Windows, macOS and Linux, the Coruna Corpus Tool is a corpus management tool that facilitates the retrieval of information from an indexed textual repository. Although it works like most concordance programs, its distinguishing feature is that it allows users to search for old or non-standard characters and tags in texts and metadata files, as well as to extract and export specific data for the purposes of research. With a new set of advanced search features and other recent improvements, researchers now have access to functionalities that significantly enhance the previous user experience.
Bidirectional Transformation of MES Source Code and Ontologies

Future production environments must be flexible and reconfigurable. To achieve this, the devices and services to fulfill the different steps of a production order (PO) should not be selected in the manufacturing execution system (MES), but in an edge component close to the shop floor. To enable this, abstract services in the PO and concrete services provided by the field devices on the shop floor need to refer to a production ontology. The creation of this ontology is a challenge of its own. This research proposes a pragmatic automation of an encoding of a primary and light weight production ontology based on the source code of MES. The transformation procedure of source code to resource, product and generic concepts of the manufacturing plant ontology is described. To this end, the knowledge of OPC UA collaborations are also exploited during the creation of resource ontologies. Due to a fundamental difference between source code implementation (imperative paradigm) and ontology representation (declarative paradigm), the problem of information loss is inevitable. This problem is overcome by formulation of production and business rules that encapsulate the logic of the MES. The foundation of ontology is exploited to formulate these rulesets using OWL based constructs and OWL based rule languages such as Semantic Web Rule Language (SWRL), Semantic Query-Enhanced Web Rule Language (SQWRL) and SPARQL Protocol and RDF Query Language (SPARQL) based on feasibility and requirements of specific rules. Further, these rulesets are either run on the automatically generated ontology at design time with an intention to enrich the knowledge base, or production runtime to validate the pre-defined business rules between the production steps. The generated ontology also acts as basis for automatically generating the OWL-S ontologies for the OPC UA application methods for the purpose of dynamic manufacturing service discovery and orchestration. The generated ontology and an abstract PO hooked with formulated rules are cached to the shop-floor network for consequent production control to enable smart edge production. An implementation is conducted on an industrial use-case demonstrator to evaluate the applicability of the proposed approach.
Long-Tailed Recognition Using Class-Balanced Experts

Classic deep learning methods achieve impressive results in image recognition over large-scale artificially-balanced datasets. However, real-world datasets exhibit highly class-imbalanced distributions. In this work we address the problem of long tail recognition wherein the training set is highly imbalanced and the test set is kept balanced. The key challenges faced by any long tail recognition technique are relative imbalance amongst the classes and data scarcity or unseen concepts for mediumshot or fewshot classes. Existing techniques rely on data-resampling, cost sensitive learning, online hard example mining, reshaping the loss objective and complex memory based models to address this problem. We instead propose an ensemble of experts technique that decomposes the imbalanced problem into multiple balanced classification problems which are more tractable. Our ensemble of experts reaches close to state-of-the-art results and an extended ensemble establishes new state-of-the-art on two benchmarks for long tail recognition. We conduct numerous experiments to analyse the performance of the ensemble, and show that in modern datasets relative imbalance is a harder problem than data scarcity.
Corrigendum “Modeling and analysis of the polluted lakes system with various fractional approaches”

The purpose of this corrigendum to the published work by the authors M.M. El-Dessoky Ahmed and Muhammad Altaf Khan (2020)[1] to correct the author name and the funding number.
Design for Empowerment: Empowering Sri Lankan Farmers through Mobile-based Information System

We developed a mobile-based information system (MBIS) to empower users to improve their livelihood activities. To do so, we first developed an empowerment framework (since one does not exist) as a basis to develop the MBIS. In particular, we conducted this research to solve an agriculture over-production problem in Sri Lanka where farmers remain trapped in a poverty cycle. They cannot make informed decisions due to lack of access to timely, context-based actionable information to achieve a good revenue. We had to generate some essential information such as current production level in real-time by capturing farmers’ decisions such as what and how much to grow. For this purpose, we needed to empower farmers to actively engage them in informed decision-making process through the MBIS. In evaluating the impact of the MBIS, we found a statistically significant positive change in farmers’ empowerment levels based on measuring self-efficacy, sense of control, and motivation before and after they used the application. Commercial organizations have since adopted this mobile-based system in India and Sri Lanka to solve agricultural problems, in universities Africa to mitigate hidden hunger, and the Corporative Research Center in Australia to develop digital health applications to manage chronic diseases, which indicates the approach’s wide adoptability.
Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition

This paper proposes a visual servo scheme for hoisting positioning under disturbance conditions. In actual hoisting work, disturbances such as equipment and load vibration are inevitable, which brings challenges to the development of a visual servo for hoisting positioning. The main problems are as follows: (1) the correlation between visual error and disturbance is not considered or well resolved; (2) the disturbance has a great influence on the control stability, but it is difficult to model. At present, there is no detailed research on the above problems. In this paper, the visual error is defined by the image error of the feedback signal based on dynamic equations containing disturbances. An adaptive sliding mode control algorithm is employed to decrease the influence of external disturbance, and the coefficient of the slide surface is established based on the adaptive gain. In view of the belief that it is difficult to model disturbance terms, a nonlinear disturbance observer is introduced to obtain equivalent disturbance. On this basis, an adaptive control algorithm with disturbance compensation is proposed to improve the robustness of the visual servo system. We use Lyapunov’s method to analyze the stability conditions of the system. Compared with the other state-of-the-art methods, the simulation results show that our method has superior performance in convergence, accuracy, and restraining disturbance. Finally, the proposed algorithm is applied to the hoisting platform for experimental research, which proves the effectiveness of the controller.
The problems of the folk-art in the information society conditions

The article analyzes the problem of the folk art existence in the culture of the information society context. The paper deals with the specifics of folk art as a type of folk art culture and its role in the formation of professional art and such types of folk art culture as urban folklore, amateurism, amateur performances. The authors shows the transformation of the artistic process in the direction of movement from a centrist, hierarchized art system to a decentralized one, network structure, suggesting the coordinated elements of the system based on the analysis of the features of the information society. The negative and positive conditions affecting the existence of folk art in the framework of the information society are indicated.
Understanding the requirement of a 3D aided augmented reality mobile app dictionary for children

Today's world is powered by advance technologies and mobile technology has become part of our daily lives. However, the usage of mobile device by children is usually for playing games, watching videos or listening to music and therefore has not been properly utilised by children to achieve educational goals. Through education, children can gain knowledge and skills that they need in their lives. Early childhood education is important because it can lead them to a better future. Edutainment is one of the popular methods to engage children in learning and this research explores the requirement of 3D aided augmented reality (AR) mobile app dictionary for children. The study is based on studying the existing popular augmented reality applications related to researches as well as conduct a questionnaire involving parents and teachers who interact with children. According to the result collected in this research, it is found that the proposed requirement will allow children to use mobile device in more effective and fun ways.
Web Scraping for Unstructured Data Over Web

The need and significance for extracting information from the web is rising up with an increasing trend. Almost every day, we end up in a circumstance, where we need to extract information from the web. This is not always about finding new courses, but we also have to prone for reviews and data for providing a brief about them. Mostly, the issue is how we scrap the data and store it, but not on the strategy we use to perform it. The project done here is a proof of concept for how we could integrate it with php to make the updating by running python on the server. Here, we are going to use the python script to extract the info from a website and parse it to get the required information regarding our needs. Then, the data is send to the webserver hosted on the internet, the php running on the server will get the data from the python script. The data is been store on the mysql server using the php script and every time, we run the python, the data in the website changes.
Security and Forensics Challenges to The MK Smart Project

MK Smart project is a joint initiative which is led by the Open university and supported by Key players such as University of Bedfordshire (Milton Keynes Campus), University of Cambridge, British Telecom (BT), Milton Keynes Council, E. ON, Anglian Water, HR Wallingford Ltd, Satellite Applications Catapult, Community Action MK, Fronesys, Graymatter and Playground Energy. The project is partly funded by HEFCE (the Higher Education Funding Council for England) and led by The Open University with the primarily aim of developing innovative solutions to support the economic growth in Milton Keynes [MK Smart]. MK Data Hub is the central infrastructure to the project which supports the acquisition and management of the big data from various data sources relevant to the city systems. [MK Smart, 2014]. Whilst the data plays crucial part to this project, its forensic value of the data held is also important to the investigation of this project. Data might be required to help in any forensic investigation to be proven in a case of Data integrity. The challenges of security and forensics to this project may be hinderance to its future. Mitigating these challenges can go a long way not only to this project but to other smart cities projects. This paper concentrates on realising the security and forensics challenges of the MK Smart project, primarily looking at the challenges of securing such a huge data on a datahub and concentrating on the best possible way to forensically investigate the large complex data such as the data stored on the Datahub.
Medical students prefer print textbooks for studying but value the e-books’ search function and availability

Since a few years our library has mostly acquired journals only in electronic format, whereas medical textbooks are often provided in print and as e-books. In order to meet the students’ current needs and to reasonably allocate financial means and efforts, we performed a survey about format preferences amongst medical students at the University of Zurich, Switzerland. 94 medical students returned our questionnaire in December 2018 (4.6% response rate). The survey showed that print books were used more often, especially for longer reading, but e-books were also commonly used. Perceived advantages of print books were the possibilities to mark text passages and better eye comfort. E-books were valued for their search function and availability.
Three-Dimensional Printed Hysteria

Seven years ago a surge of articles came out warning people of the danger of three-dimensional (3D) printed guns. This article is about how such manufactured hysteria has proven to be unwarranted and also a distraction from all of the benefits 3D printing is bringing to our society. This is an original work and provides commentary on the subject of 3D printing (including how it impacts public policy).
Automatic scoring method of English composition based on language depth perception

Abstract AutomatedEssaySystem (AES) refers to a system for evaluating and rating compositions by using techniques in the fields of statistics, natural language processing and linguistics. The selection of composition features is one of the key issues in the study of automatic composition scoring. This paper abstracts the features of language sense from the depth of language perception, which makes up for the shortcomings of the automatic scoring system of shallow features (such as vocabulary difficulty, grammar, etc.). The features of language sense are mainly the similarity of keywords extracted from the first and last paragraphs of a composition by rake algorithm, the similarity of texts calculated by cosine similarity to the context content of a composition and the similarity detection of composition language materials by automata. Finally, from the three aspects of correlation, error and overall accuracy, it analyzes whether the deep-seated language features improved the scoring ability of AES system. The experimental results show that the added depth perception features enhance the language ability of automatic scoring and are closer to manual scoring.
Object Tracking in Random Access Networks: A Large-Scale Design

We address a scenario in which networked sensor nodes measure the strength of the field generated by a number of moving objects and transmit their measurements to a fusion center (FC) in a random access manner for the final reconstruction of the objects' trajectories. To ensure scalability over an arbitrary coverage area, we divide the total observation area into design units (DUs), each consisting of several sensing cells. An extended Kalman filter is assigned to each cell, while neighboring cells communicate with each other to exchange current status, thus allowing state fusion whereby the Kalman filters adjust (overwrite) their local estimates at the end of each updating interval. In this manner, provisions are made for the objects leaving and entering a DU, yielding a system design that is scalable across a large coverage area without an increase in complexity (size) of the Kalman filters. We provide a step-by-step procedure for designing the system, taking into account the fact that sensors communicate to the FCs using random access over band-limited and imperfect channels where packet loss is inevitable due to collisions as well as communication noise. In addition, we study different rate control schemes in which the FC instructs the sensors to increase or decrease their sensing (transmission) rate in accordance with the currently estimated object locations. Performance is evaluated through simulation, showing the effectiveness of the approach proposed in terms of the mean squared localization error and data throughput, and quantifying the effect of limited bandwidth and lossy communication.
E-education as a Means of Increasing Specialists’ Competitiveness in Digital Economy

Purpose: The authors consider the features of e-education, modern e-learning tools and their role in improving the competitiveness of graduates of modern educational institutions. Design/methodology/approach: The author touches upon the competence characteristics of the teacher involved in implementing the electronic system of higher education. We consider diverse e-learning resources and their types, their advantages and disadvantages that can turn education into a more or less productive process, as well as the perspective of their use taking in the account the educational subject. Findings: The motivation of the specialist is proven significant. Potential competitiveness depends not only on the educational and cognitive motivation that drives the learning process and allows the specialist to acquire the maximum amount of knowledge and skills, but also on the professional one which provides profile orientation. Originality/value: The social significance and prospect of realization of e-education in the Russian reality, especially in the conditions of economic, geographical and social inequality, are deduced in the article. Elements of e-education, adequately integrated into the system of vocational training, can provide increased competitiveness and adaptation of the specialist in the framework of digital economy.
A METHOD FOR AUTOMATIC TEXT SUMMARIZATION BASED ON RHETORICAL ANALYSIS AND TOPIC MODELING

This article describes the original method of automatic summarization of scientific and technical texts based on rhetorical analysis and using topic modeling. The proposed method combines the use of a linguistic knowledge base and machine learning. For the detection of key terms, we used topic modeling. First, unigram topic models containing only one-word terms are constructed. Further, these models are extended by adding multiword terms. The most significant fragments of the original document are determined in the process of rhetorical analysis with the help of discursive markers. When evaluating the importance of text fragments, keywords, multiword terms, and scientific lexicon characterizing scientific and technical texts are also taken into account. A linguistic knowledge base has been created to store information about the markers and scientific lexicon. The experiments showed that this method is effective, needs a comparatively small amount of training data and can be adapted to processing texts of different subject fields in other languages.