Reviewerly/exHarmony huggingface dataset:
CREATION OF THE LINGUISTIC CORPUS OF ENGLISH LANGUAGE TEXTBOOKS

The article deals with the creation of a linguistic corpus of English textbooks. This corpus consists of texts selected from the English textbooks of the domestic authors included in the federal list. At the first stage the textbooks of Russian authors were selected. Further, a solid selection of academic texts from textbooks was carried out. All texts were manually checked for errors and typos in order to obtain the most accurate results and minimize measurement errors. Due to the materials of the corpus, it was possible to develop an appropriate metatagging of texts, which is necessary for the search engine of the Corpus Manager. Each text is located inside a separate document with an assigned code, which includes: language, subject, class, year of study, information about the authors of the textbook, text number, text genre, number of word usage and year of publication of the textbook. To date, the volume of the corpus of English textbooks is 890 texts and 980 400 word usage.
Super-resolution Thermal Generative Adversarial Networks for Infrared Image Enhancement

Thermal infrared cameras create IR (infrared) images, thus enabling the recognition of objects regardless of weather, illuminance, or ambient color. With the recent development of deep learning, research interest in image conversion and super-resolution techniques has increased. This paper proposes an algorithm that converts electro-optical (EO) images to IR images using super-resolution techniques based on generative adversarial networks. ThermalWorld data were used as the learning data. Additionally, drones of EO and IR images were added using thermal infrared cameras. The proposed super-resolution technique adapts the loss function and neural network structure to generate a high-resolution IR image. The loss function learns the neural network by utilizing the difference between the actual image and the generated image, thereby generating an image while maintaining the shape of the object on the image. The resolution is further improved by densely connecting the generator neural network structure and removing batch normalization. Finally, the structure of the discriminator is changed, and the stability of learning improved using the spectral norm. Images are generated according to each change item and quantitatively verified through performance indicators of improved image quality. This study analyzes images made with super-resolution techniques by considering the results of performance indicators and discusses the possibility of using IR images; and presents future research directions.
AFSA-SLnO Variants for Enhanced Global Optimization

Artificial fish swarm algorithm (AFSA) is a strategy which imitates the natural behavior of fish swarm in the real environment. Many improvements and modifications have been proposed on AFSA to improve its optimization performance. To date, nevertheless, the existing algorithms are still unable to achieve a satisfactory global optimum. This paper presents incorporation of circle updating position from Sea Lion Optimization (SLnO) into AFSA to enhance the robustness and optimum value. Fifteen benchmarks function have been used to evaluate the performance of the proposed variants in comparison to the standard AFSA and SLnO. The proposed variants show better result compared to the standard AFSA and SLnO.
Extended Kalman Filter Covariance Tuning Using LSTM for Navigation System

Noise covariances in the EKF (Extended Kalman Filter) act as tuning parameters due to modeling errors, such as the error generated in the process of linearizing the nonlinear system equation. Filter designers traditionally prefer the trial-and-error method. However, because the noise covariances require retuning in concert with changes in trajectories or changes in system equations, human tuning is very time consuming. Accordingly, this study proposes an automation method for real-time parameter tuning using an LSTM (Long Short-Term Memory) neural network. The proposed method consists of two stages: 1) generating residual and state error covariances from an EKF using fixed-noise covariances and 2) producing a state estimate from an EKF using tuned time-varying noise covariances obtained from an LSTM neural network. This scheme is applied to navigation systems to calculate accurate velocity, attitude, and IMU bias estimates under challenging maneuvering situations. Using simulation, we showed that the proposed method outperforms the conventional EKF of navigation systems in suppressing estimation error in different trajectory scenarios.
Nonprofit Learning Lab Train the Trainers Workshop

Nonprofit Business AdvisorVolume 2022, Issue 390 p. 4-4 Conferences & Events Nonprofit Learning Lab Train the Trainers Workshop First published: 09 February 2022 https://doi.org/10.1002/nba.31181Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2022, Issue390March 2022Pages 4-4 RelatedInformation
Interest in Bringing Legal Proceedings When Filing an Action for Revocation After Patent Expiry

Interest in Bringing Legal Proceedings When Filing an Action for Revocation After Patent Expiry Get access GRUR International, Volume 70, Issue 5, May 2021, Pages 490–496, https://doi.org/10.1093/grurint/ikab036 Published: 09 June 2021
TEACHER'S EXPERIENCE IN TEACHING SPEAKING BY USING STAD

This research was aimed to describe the teacher's experience in teaching speaking using STAD. The subject of this research was an English teacher of SMAN 10 Pontianak who was experienced in teaching English by using STAD. This research was a case study to describe the teacher's experience while using STAD in teaching speaking. The data were collected from interviews, audio-visual materials, and document reviews. The speaking score of 36 students from the Year-10 students of SMAN 10 Pontianak in Academic Year 2019/2020 was used in this research. The results showed that the STAD technique affected the students' speaking learning achievement as much as 9 out of 36 students experienced an increase in their score by 4%. Some internal factors contributed to those students' achievement, namely, students' contributions, motivating students, and improving student skills. Moreover, the findings revealed that some students did not achieve the goal of learning by using STAD. This is caused by several factors, namely managing time, managing class, and lack of speaking skills become the difficulties of this research.Keywords: Case Study, Experience, STAD, Speaking Skill.
Hyperchaotic Image Encryption Algorithm Based on Dynamic DNA Coding

A color image encryption scheme based on DNA encoding and a hyperchaotic system is proposed. First, the plaintext image is converted into three DNA matrices, and the original image pixels are shuffled by the DNA encoding and chaotic sequence, then the two DNA matrices are separately exchange of DNA fragments in rows and columns. The chaotic sequence determines the matrices and DNA fragments that need to be exchanged, which can resist statistical attacks well, and then the S-box is generated according to the DNA random code and the chaotic sequence, which maps the scrambled matrix and finally recombines the three matrices to obtain the ciphertext. The simulation experiment shows that the image encryption system has good security and can resist various attacks.
Saving Knowledge About Sources: An Efficient Method for Querying Distributed Data

Looking for information in the web of data presents a big challenge since data is distributed over a large number of RDF datasets, moreover, those datasets are remotely accessible via sources called SPARQL Endpoints where each source is being managed independently. A sought information may not be found by querying a single source, it could require retrieving its parts from several sources. Hence, having good knowledge about the performance, the efficiency and the availability of Endpoints is of paramount importance to select relevant data sources. In this paper, we present an index-based solution to process a SPARQL query on distributed SPARQL Endpoints, we focus on setting up an index presented as a knowledge base storing information to describe sources characteristics, this knowledge base is updated automatically while executing queries, thereby, we can benefit from each query execution in order to have a good knowledge of sources, as well as a benchmark to select relevant sources according to specific needs.
Boundary Accuracy of Interactive Segmentation Methods on Various Distorted Images

Quality of image is an important factor to be considered in coming up with effective segmentation algorithms. As images go through several different stages of processing such as acquisition, compression, transmission, storage, and reproduction, these digital images are easily exposed to various types of degradation leading to the deterioration of image quality. Many state-of-art segmentation methods are not prepared to handle the various level of degradation in visual quality. This paper aims to address this issue by comparing and analyzing the performance of two types of interactive segmentation methods on images with common types of distortion artifacts. The well-known Jaccard index for measuring boundary accuracy is used for evaluation of the segmentation output. The experimental results revealed interesting and unique findings of segmentation accuracy on different levels of distorted images. This indicates that the image quality plays an important role in segmentation accuracy and the need to consider the distortion effect towards achieving higher accuracy in segmentation.
Academic Inbreeding and Research Productivity in South Korea

Purpose - The purpose of this paper is to investigate the relationship between academic inbreeding and publication rates in domestic journals and international journals among South Korean academics. Design/Methodology/Approach - This paper uses a comprehensive data set on South Korean academics and negative binomial regressions. The endogeneity of academic inbreeding status is mitigated by taking advantage of data on publications in international journals prior to employment. This serves as a proxy for ability and motivation to publish. Findings - The results show that alumni appointees publish fewer international journal articles but more domestic journal articles than their non-alumni counterparts. The results support the suspicion that academic inbreeding status is confounded with ability and motivation to publish. In addition, factors such as discipline, time trends, university rank and gender are shown to interact with academic inbreeding and its impact on international and domestic publication rates. Research Implications - This paper highlights circumstances in which alumni appointees’ research productivity differs with their non-alumni counterparts. In addition, it provides evidence of the endogeneity of academic inbreeding in the research productivity function. Thus, studies that fail to account for this endogeneity are likely to estimate biased effects.
A Deep Learning Approach for Detecting Medical Face Mask on Human Faces in Response to Covid19

In this difficult time of COVID19, using a facial mask is a life saver, specially in all indoor public places. In this work, a deep learning based approach is proposed that detects whether or not a human face contains a face mask. The proposed method is capable of detecting the facial mask with $$98\%$$ accuracy for any frontal face static image or videos. The validation was performed on a variety of different scenarios to ensure the accuracy.
A Study on an Online Non-face-to-face English Reading Comprehension Class and the Learner Responses in the Untact Era

본 연구는 코로나 19 바이러스로 인해 온라인 비대면으로 이루어진 교양 영어 수업의 절차 및 구성을 제시하고이것이 지니는 학습 성취도 효과를 분석하고 온라인 비대면 교양 영어 수업에 대한 학습자들의 인식과 반응을 분석하고자 하였다. 이를 통해 비대면 교양 영어 수업의 개선 사항을 점검해 보고 언택트 시대 온라인 수업의 나아갈 방향에 대해 함께 고찰해 보고자 하였다. 본 연구에서 사례 연구로 제시한 비대면 교양 영어 독해 수업은 수업 동영상, 실시간 수업 활동, 퀴즈와 토론 활동, 줌을 활용한 화상 강의, 학생들이 직접 제작하는 영상 발표로구성되었다. 이렇게 구성된 온라인 비대면 교양 영어 수업의 효율성을 고찰하고 이에 대한 학습자 인식을 파악하기 위해 두 차례의 독해 성취도 테스트와 개방형 설문조사를 실시하였다. 그 결과 온라인 비대면으로 이루어진 교양 영어 독해 수업은 학습자들의 독해 학습에 긍정적인 영향을 미치는 것으로 나타났다. 또한 비대면 교양 영어독해 수업에 대해 학습자들의 전반적인 만족도는 높게 나왔으며 그 이유로는 교수자의 수업에 대한 준비 및 태도, 수업 내용에 대한 재미, 수업의 전체적인 구성, 영화 콘텐츠 사용, 온라인 학습의 편리성, 유용한 영어 학습으로나타났다. 포스트 코로나 시대를 대비하여 교수자들은 변화하는 교육 환경에 맞춰 온라인 비대면 수업의 다양성을확장하기 위해 노력해야 할 것이다.The aim of this study is to present the composition of an online non-face-to-face English reading comprehension class caused by the COVID-19 virus and to verify the learning effect. This study also intends to analyze learners’ perceptions of non-face-to-face English reading classes and to consider improvements for English reading classes. To this end, the first and second tests were conducted on 123 college students enrolled in a liberal arts English class, and a questionnaire was also conducted to understand the learners’ responses to this online class. The two tests found that non-face-to-face English reading comprehension classes which consisted of recorded class videos, real-time class activities, quizzes and discussions, zoom lectures, and video production presentations had a positive effect on learners’ reading comprehension learning. In addition, learners showed high satisfaction with this online class, and the factors for satisfaction included the instructor’s preparation and attitude toward class, interesting class contents, systematic class organization, and the use of movie contents, the convenience of online learning, and useful English learning. Based on these results, instructors who plan online non-face-to-face classes need to focus on well-organized preparation for class, caring for learners, and developing interesting class contents and a systematic class framework.
Improving Health Disabled People through Smart Wheelchair based on Fuzzy Ontology

People with disabilities represent a large segment of society. They suffer from many disabilities that prevent them from carrying out normal daily activities, including monitoring their health status. To improve the health monitoring of people with disabilities, this study proposes an edge-cloud-based IoT architecture that uses the smart wheelchair as an edge for real-time health monitoring and a cloud to provide more diagnosis in complicated cases that require complex data processing. We proposed a system based on this architecture that uses the Semantic Web to overcome data heterogeneity from different sources and ensure interoperability. The system also incorporates fuzzy logic to improve diagnostic accuracy by dealing with imprecision and uncertainty in knowledge.
AI-Driven Market Manipulation and Limits of the EU Law Enforcement Regime to Credible Deterrence

As in many other sectors of EU economies, ‘Artificial Intelligence’ (AI) has entered the scene of the financial services industry as a game-changer. A growing number of investment firms have been adopting AI, and particularly ‘Machine Learning’ (ML) methods, within the ramification of algorithmic trading. While AI/ML trading is expected to deliver several efficiency gains for capital markets, it also brings unprecedented risks for their safety and integrity due to some of its technical specificities and related additional uncertainties. With a focus on new and emerging risks of AI-driven market manipulation, this study critically assesses the ability of the EU anti-manipulation law and enforcement regime to achieve credible deterrence. It argues that AI trading is currently left operating within a (quasi-)lawless market environment with the ultimate risk of jeopardising EU capital markets’ integrity and stability. It shows how ‘deterrence theory’, as a normative framework, can allow us to think of innovative solutions to fix the many shortcomings of the EU legal framework in the fight against AI-driven market manipulation. In concluding, this study suggests improving the existing EU anti-manipulation law and enforcement regime with a number of policy proposals. Namely, (i) an improved, ‘harm-centric’ definition of manipulation; (ii) an improved, ‘multi-layered’ liability regime for AI-driven manipulation; and (iii) a novel, ‘hybrid’ public-private enforcement institutional architecture through the introduction of market manipulation ‘bounty-hunters’.
Offensive Language and Hate Speech Detection Based on Transfer Learning

Offensive language is a toxic content that invades the web. It is characterized by being more than a negative reaction towards a certain event or person, since it is sharp and rude. Hate speech may be implied within an offensive language if it targets a specific part of a society, a religion, a political part, a race, a background or a specific gender. The spread of such languages over the web may cause psychological effects, hate, and war and can destruct a whole community. Preventing such a virus from spreading may help to prevent many social and political disastrous effects. In this paper, we aim to detect offensive language in social media reviews. Although detecting offensive language is a task similar to sentiment analysis, the targets of each task differ, however a transfer learning between them is possible. The offensive language detection system will be based on the same characteristics as a sentiment analysis system. We enhance the classification by performing ensemble learning that addresses the outputs of many classifiers. In order to detect hate speech, we based experiments on named entities. All along experiments, we perform both supervised and unsupervised approaches, and we also vary the lexicons based on which we generate features. The herein used lexicons are either internal extracted manually or automatically from the training corpus by the authors of this paper or external collected and filtered by our system.
Novel Big Data Approach for Text Supported Service Operations Management

Operations management is being applied across all industry sectors and offers more accurate decision making, cost saving and a path to lean production. All depend on proper information, which is a valuable input for operations management and can represent the key to gain competitive advantage. Thanks to the information, we can improve and optimize processes and react to problems before they even appear. Unfortunately, getting this knowledge is often a challenging task. It is estimated that only a small portion of publicly available data can be considered as valuable. Considering the size of today's data storage, it is out of possibilities for anyone to read them all and operatively react to the changing environment and competition. In case of the text data, they are often written in many different languages, use different expert domains or customer segments, who have their own specific language style. To gain valuable information from this data, it is necessary to know each particular language (not necessarily human, but also machine generated) and grammar. This chapter presents the latest advances in artificial intelligence for the text data analysis and operations management. First, we provide the state of the art of the text processing approaches, then we discuss selected use-cases from the field of operations management and how the latest methods can help to solve those problems. The last part of the chapter outlines some ideas for further improvement of the current approaches and how to effectively analyse data in a multilingual environment and decrease memory demands.
Cultural Memory in Children’s Literature -How children’s literature remembers the Sewol ferry incident

이 연구의 목적은 아동문학이 문화학으로서 확장될 가능성을 타진하면서 아동문학이 아동들에게 어떠한 문화적 기억을 전승하는가를 살펴보는 데 있다. 이를 위해 문화적 기억에 대해 개관하고 아동문학 현상에서 문화적 기억의 역할을 논의하였다. 그리고 세월호 참사를 다룬 아동문학 작품들을 탐색하면서 우리 아동문학이 어떠한 문화적 기억을 전승하고 있는가를 분석하였다. 그 결과 우리 아동문학 작품들은 세월호에 관한 문화적 기억을 형상화하며 세월호가 남긴 고통을 발화하고 그 치유 가능성의 의문을 표함을 확인하였다. 또한 이 작품들이 세월호 참사의 희생자를 애도하고 생존자 및 유족의 정신적 외상, 국민들의 사회적 고통을 치유하고자 하며, 우리 시대의 윤리를 되묻고자 함을 밝혔다. The purpose of this study is to examine what kind of cultural memories are transmitted by children’s literature to children while examining the possibility of expanding children’s literature as cultural studies. In the end, an overview of cultural memory is provided and the role of cultural memory in the phenomenon of children’s literature is discussed. And while exploring children’s literature dealing with the Sewol ferry disaster, we analyzed what kind of cultural memory our children’s literature transmits. As a result, we came to the point that our children’s literature works embody the cultural memory of the Sewol ferry, ignite the suffering left by the Sewol ferry, and question the possibility of its healing. In addition, it was revealed that these works mourn the victims of the Sewol ferry disaster, seek to heal the trauma of survivors and their families and to heal the social suffering of the people, and question the ethics of our time.
A Path Planning for Unmanned Aerial Vehicles Using SAC (Soft Actor Critic) Algorithm

Path planning is an essential element in the autonomous flight control of unmanned aerial vehicles, where it is important to quickly establish the path in uncertain environments and avoid collisions with the terrain and obstacles. In particular, research and development of fully autonomous flight is necessary in the case of unmanned aerial vehicles performing search, reconnaissance, and detection in terrain where human intervention is difficult. This paper proposes a path planning design method using machine learning. It has the advantages of fast calculation speed and high repeatability in a two-dimensional environment. Using the Soft Actor–Critic (SAC), an algorithm based on reinforcement learning, research into machine learning, observation status, behavior, and reward functions are required to generate global paths. Additionally, the learning and path generation results are analyzed by conducting a learning-based path planning simulation in an environment with dynamic obstacles.
AESTHETIC MODUS OF G. SHPET'S PHILOSOPHY

The theses of the report are devoted to the analysis of the aesthetic views of the representative of philosophical hermeneutics, professor of Moscow University G. G. Shpet. The questions of the essence of aesthetics as a science, its relationship with philosophy and art, their role in the spiritual culture of society are considered.
Il titolo dei «Canti», i titoli dei «Canti»

This article looks back at what may have prompted Leopardi to entitle his collection of poems Canti, but even more importantly it focuses on the frequency with which the word appears in Zibaldone and in other writings by Leopardi. There emerges a close tie that the noun ‘canto’ and the verb ‘cantare’ [to sing] share with Homer’s works and Leopardi’s interpretation of them.