Reviewerly/exHarmony huggingface dataset:
Understanding Aviation English: Challenges and Opportunities in NLP Applications for Indian Languages

English is a language that is understood, spoken and used by citizens of a diverse array of countries. The speakers include both native and non-native speakers of English. NLP or Natural Language Processing on the other hand is a branch of computer science that deals with one of the most challenging aspect that a machine can process: dealing with Natural Languages. Natural languages which have evolved over centuries are complete, diverse and highly complex and thus are challenging for a computer system to understand and process. MT or Machine Translation is a more specific part of NLP that translates one natural language to another (English being one of the major researched and sought after languages among them). Though research in the field of NLP and MT has come a long way and many efficient translators are available, still Translation and other NLP applications in specialized domains such as aeronautics are still today a challenge for NLP researchers and developers to achieve. NLP applications are often used in education of English Language, and are therefore a continuous process for Non-Native speakers of English. Non-native English speakers take help of various NLP tools such as E-Dictionary, MT applications and others to better understand the English language and thus learn it better and faster. Aviation English poses a challenge to MT systems and understanding it as a whole requires specialized handling as it has own phonetic pronunciations and terminologies and constituent Out-Of-Vocabulary words. Dealing with Aviation English calls for teaming up of experts from Applied Linguistics, NLP and AI. As a result it becomes a cross-research discipline that covers situations that demand real time use of proper language, e.g. ATC communications. This Paper aims to discuss most recent research methodologies that deals with the Aviation English and reviews the problems posed by it. Being a specialized and structured form of English, the problems are faced by both native and non-native speakers of English Language. Discussion is carried out in the relevant and recent advances of methods in dealing with aviation English language challenges from both, the Human (ICAO/DGCA/AAI) as well as NLP angle. Lastly we have a look at how these challenges are linked to scope for development of applied technologies. Research in experiential Aviation English situations deals with both English for Specific Purposes - ESP (Aeronautics in our case) as well as situations in English as a Foreign Language i.e. EFL (English-Indian language pair).
Identification of the Probability Density Distribution of a Random Value

The dependence of the identification accuracy of the probability density distribution of a random variable on the number of digits (intervals) of the histogram and the size of the sample used is investigated. The bias and standard deviation of the error are estimated using simulation modeling. Quantitative results are obtained for the uniform probability distribution and the Simpson distribution. It is shown that with an increase in the histogram interval, the bias error monotonically increases, and the RMS deviation of the error has an extremum.
A Topic Detection Method Based on Word-attention Networks

Abstract Purpose We proposed a method to represent scientific papers by a complex network, which combines the approaches of neural and complex networks. Design/methodology/approach Its novelty is representing a paper by a word branch, which carries the sequential structure of words in sentences. The branches are generated by the attention mechanism in deep learning models. We connected those branches at the positions of their common words to generate networks, called word-attention networks, and then detect their communities, defined as topics. Findings Those detected topics can carry the sequential structure of words in sentences, represent the intra- and inter-sentential dependencies among words, and reveal the roles of words playing in them by network indexes. Research limitations The parameter setting of our method may depend on practical data. Thus it needs human experience to find proper settings. Practical implications Our method is applied to the papers of the PNAS, where the discipline designations provided by authors are used as the golden labels of papers’ topics. Originality/value This empirical study shows that the proposed method outperforms the Latent Dirichlet Allocation and is more stable.
DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange

Intelligent vehicles clearly benefit from the expanded Field of View (FoV) of the 360-degree sensors, but the vast majority of available semantic segmentation training images are captured with pinhole cameras. In this work, we look at this problem through the lens of domain adaptation and bring panoramic semantic segmentation to a setting, where labelled training data originates from a different distribution of conventional pinhole camera images. First, we formalize the task of unsupervised domain adaptation for panoramic semantic segmentation, where a network trained on labelled examples from the source domain of pinhole camera data is deployed in a different target domain of panoramic images, for which no labels are available. To validate this idea, we collect and publicly release DensePASS - a novel densely annotated dataset for panoramic segmentation under cross-domain conditions, specifically built to study the Pinhole-to-Panoramic transfer and accompanied with pinhole camera training examples obtained from Cityscapes. DensePASS covers both, labelled- and unlabelled 360-degree images, with the labelled data comprising 19 classes which explicitly fit the categories available in the source domain (i.e. pinhole) data. To meet the challenge of domain shift, we leverage the current progress of attention-based mechanisms and build a generic framework for cross-domain panoramic semantic segmentation based on different variants of attention-augmented domain adaptation modules. Our framework facilitates information exchange at local- and global levels when learning the domain correspondences and improves the domain adaptation performance of two standard segmentation networks by 6.05% and 11.26% in Mean IoU.
An Ensembled Anomaly Detector for Wafer Fault Detection

The production process of a wafer in the semiconductor industry consists of several phases such as a diffusion and associated defectivity test, parametric test, electrical wafer sort test, assembly and associated defectivity tests, final test, and burn-in. Among these, the fault detection phase is critical to maintain the low number and the impact of anomalies that eventually result in a yield loss. The understanding and discovery of the causes of yield detractors is a complex procedure of root-cause analysis. Many parameters are tracked for fault detection, including pressure, voltage, power, or valve status. In the majority of the cases, a fault is due to a combination of two or more parameters, whose values apparently stay within the designed and checked control limits. In this work, we propose an ensembled anomaly detector which combines together univariate and multivariate analyses of the fault detection tracked parameters. The ensemble is based on three proposed and compared balancing strategies. The experimental phase is conducted on two real datasets that have been gathered in the semiconductor industry and made publicly available. The experimental validation, also conducted to compare our proposal with other traditional anomaly detection techniques, is promising in detecting anomalies retaining high recall with a low number of false alarms.
Healthcare Information Technology

The term “health information technology” (health IT) refers to the electronic systems that store, share, analyze, and protect electronic health records. It includes also electronic prescribing, knowledge sharing, patient support tools, and mobile health technology together with decision-making algorithms. The privacy, security, and ethics of health information are priority for both patients and healthcare providers. The health IT is a target for cyber-criminals; stolen health information, or blackmailing via different kind of cryptolockers, is good on the black market. While the healthcare industry relies more on technology, cyber-attacks are threatening health IT. This article argues health IT issues from two perspectives. The first one is a physician's point of view aiming to improve quality of care using fast and accurate health IT. The second is the standpoint of cyber-security specialists aiming to protect data form cyber-criminals, continually developing new strategies and best practices.
Machine learning basics

Machine learning (ML) has become a commodity in our every-day lives. We routinely ask ML empowered smartphones to suggest lovely food places or to guide us through a strange place. ML methods have also become standard tools in many fields of science and engineering. A plethora of ML applications transform human lives at unprecedented pace and scale. This book portrays ML as the combination of three basic components: data, model and loss. ML methods combine these three components within computationally efficient implementations of the basic scientific principle trial and error. This principle consists of the continuous adaptation of a hypothesis about a phenomenon that generates data. ML methods use a hypothesis to compute predictions for future events. We believe that thinking about ML as combinations of three components given by data, model, and loss helps to navigate the steadily growing offer for ready-to-use ML methods. Our three-component picture of ML allows a unified treatment of a wide range of concepts and techniques which seem quite unrelated at first sight. The regularization effect of early stopping in iterative methods is due to the shrinking of the effective hypothesis space. Privacy-preserving ML is obtained by particular choices for the features of data points. Explainable ML methods are characterized by particular choices for the hypothesis space. To make good use of ML tools it is instrumental to understand its underlying principles at different levels of detail. On a lower level, this tutorial helps ML engineers to choose suitable methods for the application at hand. The book also offers a higher-level view on the implementation of ML methods which is typically required to manage a team of ML engineers and data scientists.
Descriptors Derived From Feedback on Teaching Mathematics in School

The purpose of this article was to identify the mechanism of bilateral feedback on Mathematics homework and to determine its impact on the success of schoolchildren learning. The following were used as the main research methods: the study and generalization of pedagogical experience which helped to consider possible ways of providing feedback on homework in Mathematics; the analysis of student artefacts with subsequent statistical processing. The latter made it possible to study the dynamics of students’ knowledge of Mathematics and to judge the effectiveness of feedback on homework. The article reveals possible ways to provide feedback on homework in Mathematics deploying digital technologies; a scheme for meaningful feedback and ensuring its bilateral nature has been developed; the influence of the feedback in question on learning success was calculated. The implications of the study involve the fact that the developed didactic conditions ensure a bilateral nature of feedback during self-directed learning of Mathematics using digital technologies. This, in turn, contributes to the efficiency of the educational process and the provision of visible learning.
Application of Virtual Reality (VR) Technologies in Management and Education

The article analyzes immersive education, augmented and virtual reality technologies and their forms of application in modern economic conditions both abroad and in Russia. Advantages and disadvantages of application of these technologies and constraints of their development are outlined. Reasons and factors inﬂuencing the application of these technologies in manage-ment and education are justiﬁed. We studied the change in interest in the concept of “virtual reality” according to the search queries of the Google trends service. The practice of AR/VR tools application in management and educational process was summarized. Statistics on the prospects for the development of the global market for virtual and augmented reality tech-nologies were analyzed. The domestic practice of development of AR/VR projects and platforms is summarized. Proposals for the development of immersive technologies in education and business are formulated.
Extraction and Evaluation of Knowledge Entities from Scientific Documents

Sciendo provides publishing services and solutions to academic and professional organizations and individual authors. We publish journals, books, conference proceedings and a variety of other publications.
On the subset sum problem for finite fields

Let G be the additive group of a finite field. J. Li and D. Wan determined the exact number of solutions of the subset sum problem over G, by giving an explicit formula for the number of subsets of G of prescribed size whose elements sum up to a given element of G. They also determined a closed-form expression for the case where the subsets are required to contain only nonzero elements. In this paper we give an alternative proof of the two formulas. Our argument is purely combinatorial, as in the original proof by Li and Wan, but follows a different and somehow more “natural” approach. We also indicate some new connections with coding theory and combinatorial designs.
Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs

Current Question Answering over Knowledge Graphs (KGQA) task mainly focuses on performing answer reasoning upon KGs with binary facts.However, it neglects the n-ary facts, which contain more than two entities.In this work, we highlight a more challenging but under-explored task: n-ary KGQA, i.e., answering n-ary facts questions upon n-ary KGs.Nevertheless, the multi-hop reasoning framework popular in binary KGQA task is not directly applicable on n-ary KGQA.We propose two feasible improvements: 1) upgrade the basic reasoning unit from entity or relation to fact, and 2) upgrade the reasoning structure from chain to tree.Therefore, we propose a novel fact-tree reasoning framework, FacTree, which integrates the above two upgrades.Fac-Tree transforms the question into a fact tree and performs iterative fact reasoning on the fact tree to infer the correct answer.Experimental results on the n-ary KGQA dataset we constructed and two binary KGQA benchmarks demonstrate the effectiveness of FacTree compared with state-of-the-art methods.
Application of electronic cases in teaching a foreign language to students majoring in Law and Economics: methodological and pedagogical aspects

The processes of globalization that have swept the whole world, including Russia, make the modern labor market extremely mobile. It poses a number of tasks for Russian higher school to train highly competent professionals who are fluent in foreign languages. Among many newest pedagogical technologies, the case-study method is viewed as promising and perspective. It appeared at the end of the 19th century in the USA, but became widespread in our country only at the turn of the 2021 centuries. The analysis of the existing definitions of this concept allowed the authors to formulate their own definition of the term electronic case, which is interpreted as a real situation prepared and loaded by the teacher into the digital learning environment of the university for subsequent comprehensive analysis and finding alternatives to its solution based on group discussions. Along with this, the authors managed to develop methodological requirements for educational electronic cases implemented at foreign language lessons at a university, to determine the range of key skills and abilities formed by the case method, to identify the advantages and disadvantages of public and non-public presentation of the case solution.
SpaceMeshLab: Spatial Context Memoization And Meshgrid Atrous Convolution Consensus For Semantic Segmentation

Semantic segmentation networks adopt transfer learning from image classification networks causing a shortage of spatial context information. For this reason, we propose Spatial Context Memoization (SpaM), a bypassing branch for spatial context by retaining the input dimension and constantly communicating its spatial context and rich semantic information mutually with the backbone network. Multi-scale context information for semantic segmentation is crucial for dealing with diverse sizes and shapes of target objects in the given scene. Conventional multi-scale context scheme adopts multiple effective receptive fields by multiple dilation rates or pooling operations, but often suffer from misalignment problem with respect to the target pixel. To this end, we propose Meshgrid Atrous Convolution Consensus (MetroCon <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) which brings multi-scale scheme into fine-grained multi-scale object context using convolutions with meshgrid-like scattered dilation rates. SpaceMeshLab (ResNet-101 + SpaM + MetroCon <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) achieves 82.0% mIoU in Cityscapes test and 53.5% mIoU on Pascal-Context val set.
Web-Based Learning Under Tacit Mining of Various Data Sources

Nowadays, many platforms provide open educational resources to learners. So, they must browse and explore several suggested contents to better assimilate their courses. To facilitate the selecting task of these resources, the present paper proposes an intelligent tutoring system that can access teaching contents available on the web automatically and offers them to learners as additional information sources. In doing so, the authors highlight the description logic approach and its knowledge representation strength that underwrites the modulization, inference, and querying about a web ontology language, and enhanced traditional tutoring systems architecture using ontologies and description logic to enable them to access various data sources on the web. Finally, this article concludes that the combination of machine learning with the semantic web has provided a supportive study environment and enhanced the schooling conditions within open and distance learning.
Research on School-enterprise Cooperation Training Model of Music Performance Professionals in Colleges and Universities Based on Repertoire Creation and Computer Multimedia Technology

Abstract Along with the development of Internet technology, computer has been deeply into people’s life, the computer is gradually changing people’s way of life to live in along with the development of Internet technology in the field of teaching. The traditional classroom model has been changed. The integration of music performance majors in colleges and universities into the Internet makes professional teaching more convenient and convenient making the spread of music performance more extensive. The life and study of naturalized citizens have been inseparable from the computer multimedia technology, we can see the importance of computers.
The Students’ Perspective of Using Instagram as a Writing Assignment Platform

This study aimed to find out the students’ perspective of using Instagram as a writing assignment platform. Using a qualitative research method, this study involved the students of the 2nd semester in English Department Universitas Islam Syekh-Yusuf Tangerang. There were 61 students in writing class who were involved as the participants. It used the technique of survey with open-ended questionnaire as the instrument of data collection The results show that almost all students have positive perspective towards Instagram as a writing assignment platform. The participants found out that Instagram was fun, easy, and effective media to be used in doing writing assignment. Although, there were also some barriers in using it such as, bad internet network and privacy issue. It can be concluded that, Instagram can be one of useful teaching media, especially for writing, as long as it is used and supervised appropriately.
Innovative Teaching Technologies as a Way to Increase Students’ Competitiveness

The article presents an analysis of innovative teaching technologies as a way to increase students’ competitiveness. The author found that innovative technologies in education are information and communication technologies relying on computer-based learning. The structure, content of educational software, organization of Web-space are important when using innovative teaching technologies in English classes. We conducted the study in several stages: comparative analysis, synthesis, classification and systematization of the results of psychological and pedagogical, educational and methodological research; study of legislative acts, periodicals in order to identify the state of the research issue, and determining the directions of its solution, as well as subject, goal and objectives of the study. We used modelling to create situations of foreign language professional communication of future IT specialists. Empirical methods involved questionnaires used for identifying the motives of professional development and determining the features of the educational activities of future IT specialists in the process of training. The methods of mathematical statistics allowed to scientifically describe and systematize the obtained data, to identify the quantitative relationship between the studied phenomena, to analyse and summarize the results. We conducted a socio-psychological study during 2016 - 2019. It involved 255 first- and fourth-year students of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute.” Innovative information and communication technologies that improve the educational and cognitive activity of students, as well as increase the level of their knowledge have become important in teaching a foreign language in higher educational institutions. These technologies include MOODLE — Modular Object-Oriented Dynamic Learning Environment, business game, integrated pedagogical technology, case study technology. Thus, the information-rich learning process in combination with the use of innovative technologies, well-organized e-learning, interactive training courses, multimedia tools improves the program of teaching and learning foreign languages in general, and English in particular, improves the level of knowledge of future IT specialists and motivation to study and learn foreign languages, allows students to use a variety of authentic materials. We state that all these factors influence the process of individualization of learning and contribute to the successful mastery of a foreign language.