Reviewerly/exHarmony huggingface dataset:
THE EXPERIENCE OF THE FOREIGN LANGUAGE TEACHING AT KRASNOYARSK STATE AGRARIAN UNIVERSITY ON ZOOM PLATFORM IN THE PANDEMIC CONDITIONS

At the present stage of the development of world civilization, it is problematic to find a field of activity that is not affected to some extent by the consequences of the spread of a new coronavirus infection. The previously unthinkable situation has become a serious and critical test of readiness not only for the world health service, but also for educational institutions at all levels. General education schools, colleges and universities were forced to transform the education process into a new format, experimenting with mass “remote learning”. One can assert that the impact of the pandemic on education, including higher education, has radically changed its foundations. all educational institutions, including universities, found themselves in difficult conditions and were forced to adapt to the current events in a short time, spend significant financial and human resources for accelerated digitalization of education, trying to maintain the quality of education in the current conditions. Educational information and communication technologies, which seemed to be a form of education of the future, also changed their former essence and purpose. Universities were forced to solve many pressing issues in a short time: in what forms to conduct distance learning; what technical means to use for this; how to assess the assimilation of students of the material received; how to conduct final exams, defense of diploma projects, and how to conduct the enrollment for the next academic year. Krasnoyarsk State Agrarian University did not stay away from the events. And if in March 2020, the university mainly conducted remote training on the Moodle platform, designed more for independent work of students, then during the second wave, the university successfully conducts distance learning on the modern Zoom platform. The purpose of the article is to show the experience of using the above-mentioned platform in teaching foreign languages, considering its advantages and disadvantages.
Rank Data Clustering Based on Lee Distance

In this paper, we consider a cluster analysis for complete rankings of N items that aims to identify typical groups of rank choices. The “K-means” procedure based on Lee distance is studied in details and several asymptotical results for large values of N are derived. An algorithm for approximating the normalizing constant in the clustering procedure is proposed by using some properties of Lee distance. In order to compare the clustering method based on Lee distance to those based on other distances on permutations, we apply the presented procedure to a data set obtained from the results of the American Psychological Association presidential election.
Design of website-based library information systems at SMK Pasundan Jatinangor

Abstract SMK Pasundan Jatinangor is a private vocational high school located in Jatinangor District. Currently the system in effect in the place under study is semi-computerized based, when a book is borrowed in the library the data is inputted in Ms. Excel, as well as member data, book data, date of borrowing and date of return are inputted manually which resulted in the calculation of fines being calculated manually. So that some significant obstacles are found, including 1.can be lost because the book does not automatically update 2. When there is a loan, there is an error recording book data and member data 3.Lower handling of services in the library 4. A error in late fines for returning books 5. requires a long process of inputting books, members and borrowing. In this study, the authors use a system development tool using UML (Unified Modeling Language) which consists of: Use Case Diagrams, Activity Diagrams and Class Diagrams. With the design of this library information system, it is hoped that it can make it easier for officers / librarians to monitor the availability of books, add books, borrow and return books, so that they can assist in library services and management.
Realization of Recommender Framework Based on Community Detection

Recommender systems play an important role in suggesting relevant information to users based on their available preferences about items. Utilizing a recommender system allows companies to increase revenues, customer satisfaction and enable personalization and discovery. Content-based and collaborative filtering approaches are the most popular techniques in recommender systems predicting users preferences based on “collaborative” data about users and items in the system. However, their use is not justified in certain applications, particularly when user-item collaboration data is very sparse or missing. In this paper, a recommender framework based on community detection is developed outperforming other popular recommendation methods in some applications.
Formation of management forms in the pedagogical process

The article is devoted to the history of the formation of forms of management in the Russian pedagogical process. The authors focus on the development of pedagogical problems in the late 19th - early 20th centuries and school reform in the period under review.
Introduction to machine reading comprehension

Machine Reading Comprehension (MRC) is a technique that uses algorithms to enable computers to understand the semantics of articles and answer related questions. Because the articles and questions are in the form of human language, MRC lies within the scope of Natural Language Processing (NLP), and is one of the latest and most popular topics. In recent years, with the development of machine learning, especially deep learning, the research on MRC has seen significant progress and has emerged in various applications. This chapter will introduce the concept of MRC in the background of NLP and deep learning. Then, we will discuss the evaluation of MRC and related datasets.
Usando tecnología móvil para el aprendizaje de la estructura de los elementos Químicos

In Mexico the use of mobile telephony has increased in recent years, which gives the idea of the impact they have on daily life, encouraging the user not to be in a predetermined place and are a step towards learning at any time and in any place. It is important to create applications using this type of technology, to support the education sector. This research, which was conducted under a mixed-type approach, used as tools for data collection to the questionnaire, observation and interview, resulting in an application that made use of mobile technology, focused to promote the learning of the structure of the chemical elements, as support to the students of baccalaureate, in the subject of Chemistry; This is because young people depend on the printed format that contains the general scheme of all the symbols or memorization to learn each of the elements and their parts. Although on the Web there are several applications that circulate for free on the same subject, some are restricted to computer equipment and others were created for smartphone phones that require a payment to download them.
Knowledge Graph Based Teaching Analysis on Circuit Course

As a basic specialized course, circuit course has a wide range of knowledge and multiple theories, which may be difficult for students to understand and learn. With the advent of “Internet plus Education”, the reality of rapid growth of massive learning resources and limited learning time has exacerbated the problem of information confusion and information overload. This paper proposed a solution to the above problems by applying knowledge graph technology in circuit course teaching. Through constructing the interdisciplinary and multimodal knowledge graph of circuit course, it could support upper applications such as knowledge point retrieval and visualization, personalized learning path recommendation and intelligent teaching analysis. Consequently, by utilizing knowledge graph technology, it will provide a new way to assist teachers to carry out targeted teaching and assist students to implement personalized learning.
Visual Servoing Using Virtual Space for Both Learning and Task Execution

In this paper, we describe a framework for per-forming an object picking task using visual servoing. As a robotic manipulator approaches the object to be grasped, a convolutional neural network (CNN) is used to generate motions to utilize visual servoing. However, to obtain an appropriate CNN, it is necessary to prepare a large amount of training data. Therefore, we propose a method that utilizes a virtual environment to reduce the load. Moreover, while performing the actual object picking task, sensor data acquisition and motion generation are performed using the virtual environment. This renders approaching the object possible even when the texture changes in the actual environment where the robot moves. An object grasping experiment was conducted on a rectangular box or a cylindrical object, and the performance of the proposed framework was verified.
The Actor-Critic Algorithm for Infinite Horizon Discounted Cost Revisited

Reinforcement Learning (RL) is a methodology used to solve Markov decision processes (MDPs) within simulators. In the classical Actor-Critic (AC), a popular RL algorithm, the values of the so-called actor become unbounded. A recently introduced variant of the AC keeps the actor's values naturally bounded. However, the algorithm's convergence properties have not been established mathematically in the literature. Numerically, the bounded AC was studied under the Boltzmann action-selection strategy, but not under the more popular ε-greedy strategy in which the probability of selecting any non-greedy action converges to zero in the limit. The paper revisits the AC framework. A short review of the existing literature in the growing field of ACs is first presented. Thereafter, the algorithm is investigated for its convergence properties, under ε-greedy action selection, numerically on a small-scale MDP, as well as mathematically via the ordinary differential equation framework.
What is machine vs human rationality?

This chapter studies the essence of machine and human rationality by looking at the relevance of information, intelligent machines, and optimization for the formulation of the concept of rationality. Rational agents maximize their net utility. This chapter also studies the notion of utility and how it is formulated. The uniqueness of a utility function is also elaborated on. It concludes that the formulation of utility is subjective, and this has implications on the essence of rationality.
What was the “Appendix B” theory of types for?:

In the “Appendix B” to his Principles of Mathematics (1903), Bertrand Russell developed a theory of types that is different explicitly from his so-called “ramified” theory of types in Principia Mathematica (1910). It is not easy to evaluate properly this “Appendix B” theory of types, because (A) it is sometimes thought that it is only a rough sketch added hastily, and (B) it seems to play no role for Russellʼs later theoretical developments. But in this paper, I shall show that both (A) and (B) are not correct and that the “Appendix B” theory of types played an important role for his theoretical developments leading to the ramified theory of types.
Leveraging Clinical Notes for Enhancing Decision-Making Systems with Relevant Patient Information

Personalised treatment is usually needed for hospitalised patients afflicted by secondary illnesses that demand daily medication. Even though clinical guidelines were designed to consider those circumstances exist, current decision-support features fail to assimilate detailed relevant patient information. This creates opportunities for the development of systems capable of performing a real-time evaluation of such data against existing knowledge and providing recommendations during clinical treatments. Herein, we describe a proposal for a new feature to be integrated with electronic health record (EHR) systems which can enrich the health treatment process through the automatic extraction of information from patient medical notes and the aggregation of this novel information in clinical protocols. The purpose of this work is to exploit the historical component of the patient trajectory to improve the performance of clinical decision support systems.
The Political Economy of an Academic

A sense of excitement buzzes around the classroom as students await the announcement of closures around Tampa Bay. However, students are yet to consider the full impact of a deadly virus. Their excitement is concerned with Spring Break because any announcement is likely to add a few more days to a much-deserved break during which students will pick up extra hours at their just-about-minimum-wage jobs. I humor students that I neither have control over the announcement, nor information about what will happen if-and-when we resume after a closure. We are oblivious about the situation: the pandemic is still an epidemic in China, and we live in the United States. I row in a similar boat as students with pay that barely pays, and work that expects work. As a Visiting Instructor at the institution I graduated from, I am in a liminal space in the lower order of the academic...
The Impact of Footnotes on Readership: With Reference to Sinhalese Translation of ‘The Sun Also Rises’

Using footnotes can be identified as an essential compensation strategy to translate the untranslatable despite the general opinion of readers that footnotes are a hindrance. Footnotes occur at the bottom of the page as an explanation to present the text to the reader. The present research tries to investigate the impact of using footnotes in the translation on readership and thus it aims at finding whether it provides the facility of comprehensive reading. In-depth interviews were carried out towards a sample group of 24 readers belonging to different educational backgrounds and age groups. It was identified that footnotes were used to symbolize cultural background, time frame with thematic concerns, French and Spanish terms, places, and situational phrases and words. Thus, usage of footnotes has been used as a translation strategy to introduce and render the foreign cultural essence to the local Sinhalese reader. Qualitative results of the sample group showed that readers struggled to a great extent to read without footnotes. The readers who were provided with footnotes have been able to continue reading smoothly due to sufficient explanations; however, some readers gave negative comments about the use of footnotes. The sample group has also provided recommendations and suggestions. Based on the results of the present study, it can be concluded that footnotes have both positive and negative effects on the readership.
MICROS: Mixed-Initiative ConveRsatiOnal Systems Workshop

The 1st edition of the workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS@ECIR2021) aims at investigating and collecting novel ideas and contributions in the field of conversational systems. Oftentimes, the users fulfill their information need using smartphones and home assistants. This has revolutionized the way users access online information, thus posing new challenges compared to traditional search and recommendation. The first edition of MICROS will have a particular focus on mixed-initiative conversational systems. Indeed, conversational systems need to be proactive, proposing not only answers but also possible interpretations for ambiguous or vague requests.
Air Writing: Tracking and Tracing

Writing is an integral part of learning. Its value is quintessential in the development of cognitive understanding which aids in absorbing and de-mystifying the world around us. Air writing as a concept has been prevalent in education systems all across the world; teachers and parents have been training children to gesture the shapes of letters and spell out words in the air to build the habit of writing, but its technology is relatively recent. In this paper, we have introduced a method to implement air writing which allows users to trace linguistic characters in a pen-up pen-down motion of finger or hand thereby eradicating the need to write continuously. The proposed method is not frustrated by the requirement of extra hardware like a data glove or a specialized camera. The implementation makes use of the in-built camera in personal computers, detects the hand region, and traces or tracks the hand trajectory as per the user’s requirement. The implementation is broadly divided into two parts: (i) Detection of hand region; and (ii) Tracking and tracing hand movements. We propose the use of Single Shot Multibox Detector (SSD) with MobilenetV1 as the base network to detect the hand region. For the second half of the implementation, we have used the deque data structure and OpenCV. We have also included the option of complete/partial erasing of the screen to simplify the process of air writing and make it more intuitive.
A Neural Network Model to Identify Relative Movements from Wearable Devices

In this paper, we propose a simple neural network model to identify and analyze movement based data collected from wearable devices. The base variables used for the analysis include the relative altitude, the accelerometer acceleration, quaternion, the relative motion of gravity, the user acceleration, and the relative gyro rotation in an x, y, and z plane. Based on previous related studies, extensive feature engineering was utilized to generate 171 unique features from those previously mentioned, which showed a dramatic increase in accuracy. The Label Smoothing Cross Entropy feature was also utilized to make a better generalization of the proposed model. We were able to achieve 95% average on our test data sets which out performed a previous similar work by a good margin of 20%.
Performance of Two-Link Robotic Manipulator Estimated Through the Implementation of Self-Tuned Fuzzy PID Controller

Robot manipulators become intensified major segments in the production sectors that are utilized in numerous applications such as welding, granulating, mechanical dealing, and assembling because of velocity, accuracy, and repeatability. These applications necessitate a legitimate path plan, appropriate generation of direction, and above all a control design. In a manipulator system, the links are expanded based on efficiency and versatility is upgraded alongside the complexity. This controller tuned to produce the least weighted sum of integral of absolute error and component of positive change in controller output. The controller has the advantage of examining and controlling a highly non-linear and rigid manipulator system. To optimize the system, a genetic algorithm is being used to provide an actual and desired result by incorporating a combination of a genetic algorithm with self-tuning fuzzy. PID is composing the controller much more superior and reliable.
A Scalable Deep Neural Network to Detect Low Quality Images Without a Reference

Online streaming services have been growing at a fast pace. To provide the best user experience, it is needed to detect low quality images from videos so that we can repair or improve them before showing to customers. For example, for movie and TV-show streaming services, it is important to check if an original (master) video produced from a studio has low quality images that contain artifacts such as up-scaling or interlacing; for live streaming services, it is important to detect if a streamed video have hits due to encoding such as H.264 or MPEG-2. The impairment detection is usually measured by the nonreference (NR) metrics because it is often difficult and sometimes impossible to get the original (master) videos. On the other hand, today researches in the image quality area, such as super-resolution, are mainly focused on the full reference (FR) metrics like PSNR or VMAF. In this paper, we present an algorithm that is able to reliably compute five types of spatial NR metrics that are commonly used in the online streaming industries. The algorithm consists of two components: a pre-processing step that spatially de-correlates pixel intensity values and a novel deep neural network (DNN) that is able to quantify the NR metrics at the image region level. We show that our algorithm achieves better performance than state-of-art algorithms in this area.
Diversity Aware Relevance Learning for Argument Search

In this work, we focus on retrieving relevant arguments for a query claim covering diverse aspects. State-of-the-art methods rely on explicit mappings between claims and premises and thus cannot utilize extensive available collections of premises without laborious and costly manual annotation. Their diversity approach relies on removing duplicates via clustering, which does not directly ensure that the selected premises cover all aspects. This work introduces a new multi-step approach for the argument retrieval problem. Rather than relying on ground-truth assignments, our approach employs a machine learning model to capture semantic relationships between arguments. Beyond that, it aims to cover diverse facets of the query instead of explicitly identifying duplicates. Our empirical evaluation demonstrates that our approach leads to a significant improvement in the argument retrieval task, even though it requires fewer data than prior methods. Our code is available at https://github.com/fromm-m/ecir2021-am-search .
Breaking Down Barriers Through the Creation of an International Digital Library for Children

A research project, begun in the fall of 2002, expects to tap the potential of the Internet for breaking down barriers and building tolerance and understanding through access to exemplary children’s books from all over the world. The creation of the International Children’s Digital Library (ICDL) is the focus of a five-year project being conducted by the University of Maryland/College Park and the Internet Archive with funding from the US National Science Foundation (NSF) and the Institute for Museum and Library Services (IMLS). This paper describes the project, discusses initial research findings and outlines the direction of ongoing research.&#x0D; Jessica Anthony, and Brewster Kahle
Joint Extraction Model of Entity and Relationship Based on Reinforcement Learning

In this paper, an entity relationship association extraction model based on reinforcement learning is proposed, namely, the sequence labeling Association model. It is used to solve the tag noise caused by the entity relationship extraction method based on remote monitoring. The model consists of two modules: sentence picker module and entity relation joint extraction module. First, the sentence picker module selects high-quality sentences with no data noise. Joint entity relationship extraction module will then selected sentences as its input and sequence annotation decoding method to forecast the input sentence and the sentence selected modules provide reward and guidance sentences selected sentences of choice high quality, and adopted a special label scheme combined extraction task is converted into a sequence labeling problem. Finally, the combination model of sequence annotation is trained to optimize sentence selection and sequence annotation together. The experimental results show that the sentence picker can effectively deal with the noise of the data, and the combination model of sequence annotation can effectively improve the F1 value of the sequence annotation model.