Reviewerly/exHarmony huggingface dataset:
Extended Abstract: Analyzing Prescriptivism in Usage Guides: Implications for Tech Comm

Understanding the level of prescriptivism with which a usage guide treats usage rules can help technical editors make rhetorically informed choices about whether to follow the guide’s advice or not. In this extended abstract, I review a previous study I carried out analyzing levels of prescriptivism in usage guides [1], and I discuss implications that this kind of research can have for technical communicators. Specifically, I outline possible future research that might relate more directly to tools technical communicators use, and I offer a sample pedagogical exercise teachers can use to help students think more critically about prescriptive usage advice.
Designing A Chatbot Application For Web-Based English Learning Using Boyer Moore Algorithm

In the current era, the ability to use English is very important because English is not only used to communicate but also as a source of information. However, many students face challenges during the English learning process, including monotonous and less interactive learning methods. Therefore, an interactive and accessible Chatbot-based application is needed to help learn English. In this application, Boyer-Moore algorithm is used for keyword pattern matching of questions with knowledge base. Boyer-Moore algorithm is a string search algorithm that optimizes character matching from right to left. The results showed that the application of Chatbot application using Boyer-Moore Algorithm can answer questions with an accuracy rate of about 96% from a total of 100 questions tested.
Extended Abstract: UX Writing and Design: Dissolving Boundaries

During these times of rapid technological change, one needs to examine the manner in which new electronic mediums now shape how the message, written content (a medium contained within other electronic mediums), is communicated on human computer interfaces off backlit screens. While doing so, one as well needs to address the challenges affordances distinctive to backlit screens and electronic applications pose for users. Dissolving boundaries between design and writing can address some of these challenges by creating opportunities for using the familiar to move users into new digital realities and, in the process, produce clarity for users by bringing writing and design closer to users’ experiences and knowledge. That, in turn, can empower users in new digital environments.
Software Application for Learning Esperanto as an Example of Education Gamification

Modern educational systems need methods of stimulating and encouraging schoolchildren and students to acquire new knowledge and apply it in their future professional activities. One of these methods is the use of gamification techniques in the educational process to award students and inspire them to further master the educational courses. The methods of education gamification are analyzed and the development of a software product for the Android operating system, designed to learn Esperanto in game form, is presented in this article. The application is an example of using the point-rating system of student accomplishments and competitive approach to learning. In addition to theoretical lessons, it contains practical exercises with tasks for better mastering of the material, for the correct completion of which the student receives a reward. Points received for correct solutions increase the player's rating and can be spent on decorating the game. The authors underlined the advantages and disadvantages of gamification in the students' educational process, as well as the development prospects of this method.
The Phenomenon of Clip Thinking and Its Role in Electronic Educational Environment

On the basis of existing literature data, the paper considers the process of formation of the modern understanding of the phenomenon of clip thinking. Based on the experience of researchers of this phenomenon, various interpretations of clip thinking are presented, as well as the given studies are devoted to the positive and negative features of this type of thinking. The influence of clip thinking on the work of modern Internet mass media is considered. The potential risks associated with the spread of clip thinking are indicated. The process of taking into account the factor of clip thinking expressed in the mosaic of consciousness and the possibility of multitasking in the modern educational environment was considered. The prospects for adapting the educational process under the dominance of clip thinking are outlined. The conclusion indicates the current progress in the knowledge of clip thinking and the problems faced by researchers of this phenomenon.
Options Analysis for the Use of Solar Generation Systems on the Territory of Hydropower Plants

In this article the options for the useful use of the area of hydraulic structures and the adjacent territory of hydropower plants in order to accommodate photovoltaic panels are analyzed. The own needs of the hydropower plant are the main consumer of power for photo panels. Potentially accessible places for installation of photo panels are considered on the example of the Ust-Kamenogorsk hydropower plant. The principles and criteria for preliminary screening are formulated on the basis of a qualitative analysis of options for placing photo panels. A quantitative analysis of the technical potential of solar radiation and an assessment of the degree of coverage of the hydropower plant's own needs with the help of photo panels over the annual interval were made. Based on the results obtained, recommendations were formulated for the optimal location of photovoltaic panels on the territory of a hydroelectric power plant, in particular, on its hydraulic structures.
Does Context Matter?

Digital competence is one of the most fundamental competences pre-service teachers should have. Training programs have been carried out to support digital competence. It is also important to examine how pre-service teachers plan to use the knowledge and skills that they have gained from the digital competence training in different contexts. Thus, this case study examined the pre-service teachers' perspectives on personal, pedagogical, and professional digital competence in digital competence training. The study was conducted with 30 pre-service teachers. As a result, the digital competence training supported pre-service teachers to develop a perspective for reaching their personal, pedagogical, and professional goals through activating their information and data literacy, communication, and collaboration, digital content creation, safety, and problem-solving competence areas.
Horizons of Development of Physics Education of Young People Throught the Prism of the Big University of Tomsk

The Russian Federation has approved five national education goals, one of which assumes, among the targets characterizing its achievement, ensuring the country's presence among the top ten countries in the world in terms of research and development, including through the creation of an effective system of higher education. This paper discusses the project “Big Tomsk University”, which provides for the formation of a new concept for the development of a technological university. In order to achieve national goals, it is planned to cooperate with all the leading universities of the city among themselves. In this paper, the current directions of all leading Tomsk universities in the “Priority 2030” program are considered. Also, special attention in this work is paid to the need to improve the quality of teaching physical and mathematical disciplines in schools. To achieve this goal, there are physics and mathematics schools on the basis of some universities in the city.
Using Machine Learning Techniques for Assessing Media Text Readability

The paper addresses the problem of assessing media text readability depending on the correlation of objective and subjective text complexity. Objective text complexity refers to a set of measurable characteristics such as baseline, morphological, syntactic and lexical characteristics of a media text (a piece of news published on university websites), which is calculated automatically for each text. In total, 34 parameters are considered. Subjective text complexity score is assigned to each text by human experts who assess the clarity, structure, cohesion and coherence of the text. The correlation between subjective and objective text complexity is studied using machine learning models. The readability level is measured using machine learning methods and regression-correlation analysis. Namely, an artificial neural network and regression models are used. It is demonstrated that the use of polynomial features and lasso regularization allows to get a compact regression model of a high quality. The use of the chosen machine learning techniques made it possible to estimate the impact of distinct linguistic features on the readability level.
The Combined Approach to Identifying Argumentation Structures in Short Scientific Papers

The paper described the method for identifying argumentation structures in scientific texts in Russian language. This approach is aimed at automating argumentation annotation of text sets. It combines the machine learning methods and rule-based search through patterns that are based on a dictionary of argumentation markers. The combined method covers the four stages of modelling the argumentation structure of a text by an annotator: 1) identification of statements in a text, their classification into argumentative and non-argumentative; 2) detection of statements connections; 3) specification of statement roles in arguments (premises, conclusions); 4) identification of exact reasoning model in an argument (Analogy, Example, Verbal Classification). Stages 1) and 3) employ the machine learning methods (LogReg, SVM, MLP, MNB), the stage 2) uses the markers dictionary, the stage 4) relies on search patterns in form of regular expressions. The dataset for evaluating the method consists of argumentation annotations for 29 texts of short scientific papers from two thematic areas (information science and linguistics). These annotations are constructed by human experts with the ArgNetBankStudio platform and contain 1259 arguments and 1309 statements. The paper provided the quality scores for the identification of argumentation components at every stage. These scores showed that the combined method is applicable to the partial automatization of annotating argumentation.
Videoverification Module for Bank Customer

Modern technologies are actively developing and are including in many fields. The spread of remote provision of most services requires increased attention to ensuring the security of user data, as there is currently an increase in cybercrime. Some companies in different fields and using different means use video identification for customer service. But the decisions of most of them have nothing to do with protection from the activities of fraudsters. And the most vulnerable to the actions of cybercriminals is the credit and financial sphere. A neural network-based solution has been proposed to protect the bank's customers from banking fraud through remote banking service. This paper describes the software product “DoubleCheck” which allows using video identification with high accuracy to confirm the identity of a client who wants to perform some banking operation. “DoubleCheck” is based on the Meso-4 neural network architecture and allows you to effectively distinguish the image of a bank customer from a DeepFake image.
Using Mixed Reality for Development of Instruction for Industrial Unit Maintenance

In modern complex manufacturing always rely on skilled workers. But training new specialists in complex tasks is an actual problem. In virtual simulation training can be done multiple times and without life risk. Nevertheless, training on the computer is not the same as in the physical world. Blend both ways of training can Mixed Reality. In this paper one of the variants of developing application-instruction for industrial unit maintenance in the form of Mixed Reality application is shown in detail. Used instruments and platforms, where key components were created, are presented. Software structure of the application is described as well as implementation of The State Machine design pattern. This pattern was adapted for application needs, code snippets are shown. The application software was written in the C sharp programming language. The instruction is an immersive training for future specialists. Operator is following the steps of the instructions using the user interface - main virtual panel. Virtual panel design prototype, its features and realization in application are demonstrated. To create an interface related design task was solved including functional components. The connection between essential components is described. Operator's work in application is also demonstrated.
Towards Controlled Natural Language for Event-Driven Temporal Requirements

Currently, requirements for control software written in natural language are often formulated ambiguously and incompletely. Controlled natural languages (CNLs) can solve this problem, at the same time maintaining flexibility for writing and conveying requirements in an intuitive and common way. The creation of domain-specific controlled natural languages nowadays is under active development. In this paper, we suggest CNL which is based on event-driven semantics. This language is intended for describing the temporal properties of cyber-physical systems. We develop our CNL using a number of natural language patterns build for classes of requirements expressed in Event-Driven Temporal Logic formalism (EDTL). Due to formal semantics of EDTL, the suggested CNL is also unambiguous and can be translated into logic formulas. As a result, the proposed CNL provides an auxiliary tool to improve communication quality between different participants in the industrial system development process: customers, requirements engineers, developers, and others. Therefore, the solution helps to reduce the number of errors in the formulation of requirements at earlier stages of development.
Psychological Aspects of Digital Technologies in Education

The article is devoted to a theoretical review of the problems of psychological aspects of digital technologies in education. The authors list the features of the representatives of the “digital generation”, highlight the most important and socially significant trends characteristic of higher education in the current digital era, as well as the problems of digitalization of education. The conducted theoretical research shows that the digitalization of education leads to ambiguous psychological consequences for the subjects of the educational process. The most important psychological aspect of digital education is a fundamental change in the nature of communication of the subjects of the educational process, which determines its positive and negative effects. The results obtained indicate the importance of taking into account the psychological aspects of digitalization for specific educational programs, for the organization and filling of the content of various forms of classes by teachers of higher education organizations and also determine the need and directions for optimizing interpersonal interaction of its subjects.
Perancangan Sistem Informasi Pendataan dan Pelayanan Pendaftaran Pasien pada Klinik Pratama Lina Berbasis Website

This research aims to design a web-based information system for patient data management and registration services at Klinik Pratama Lina. The study involves the analysis, design, and implementation phases to meet the needs of patient data management and registration in the clinic. The designed system includes interfaces for doctor registration, doctor data management, patient data management, treatment data management, and treatment report generation. The Waterfall method is utilized as the software development approach in this research. The study utilizes a sample dataset of 10 patients. The results demonstrate that the web-based information system for patient data management and registration services can enhance the efficiency and effectiveness of the patient registration process and data management at Klinik Pratama Lina. However, the research also identifies some limitations that need to be addressed, such as the possibility of input errors by users. This research contributes to the development of health information systems that can improve the quality of service and patient data management in the clinic.
FORENSIC LINGUISTICS IN UNIVERSITY STUDY PROGRAMS: NEEDS, POSSIBILITIES, AND LIMITATIONS

This paper explores the increasing demand for specialists in forensic linguistics, highlighting the significance of language in forensic investigations. It examines the requirements of the job market and the incorporation of forensic linguistics into university study programs. Additionally, the paper discusses degree programs related to language andтforensic linguistics, as well as the tools and technologies which support linguistic research in this field. It also addresses the lack of standardized education concerning forensic linguistics, the insufficient number of specialists, and the complexity and time-consuming nature of linguistic research in forensic investigations.
Using Dummy Locations to Conceal Whereabouts of Mobile Users in Location-Based Services

Location-based services are extremely popular in these days. Mobile users use the services such as GPS and Google Maps almost every day to assist their daily activities like finding a restaurant or looking for an apartment. However, in order to use the services, users have to share their location information with the service providers. This requirement may hold back the service adaptation since users may not like to share their locations with others. One common method to preserve user privacy is to send a couple of dummy locations along with the true location to the service providers, so the provider would not be able to tell which location is true. This method is simple and effective, but it also has some drawbacks that make the privacy safeguarding fragile. If the dummy locations are not carefully planned, they may land on wild fields or water and could be easily perceived as fake. This research investigates the flaws of using dummy locations to uphold user privacy from both the users’ and service providers’ points of view, and proposes innovative methods to close the loopholes, so more users will be willing to use location-based services.
Towards Low-Cost Learning-based Camera ISP via Unrolled Optimization

Recently, learning-based image signal processor (ISP) pipelines modeled using convolutional neural networks (CNNs) have been able to provide higher quality images over traditional model-based ISPs at the expense of significant memory, energy, and computation overhead. We propose an unrolled optimization network that models the ISP pipeline with considerably lower number of parameters and computation overhead. The unrolled optimization solves the image reconstruction problem of the ISP by leveraging both model-based and learning-based methods. In the proposed ISP model, the image formation operators namely, blur kernels and sensor sampling functions are formulated with learnable parameters such that the physical constraints are respected during conventional training. A CNN that is shared across the iterations of the unrolled model plays the role of the prior and performs denoising. An efficient tone mapper network is also utilized to further improve the quality of the resulting images. The entire pipeline is then trained in an end-to-end fashion using perceptual loss. The proposed ISP has over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{34}\times$</tex> fewer parameters in comparison to the state-of-the art deep ISPs.
Cyberbullying Detection on Social Media Using Machine Learning

Social media platforms have seen an increase in the prevalence of cyberbullying. Making social media platforms safe from cyberbullying is essential, given the popularity and extensive use of social media among people of all ages. This study compares three machine learning algorithms, Support Vector Machine (SVM), Na¨ive Bayes, and a Bidirectional Long Short-Term Memory (Bi-LSTM) on a cyberbullying Twitter dataset. Regarding the experimental results, Bi-LSTM model performs the best, achieving 98% accuracy, followed by SVM with 97% accuracy, and Naive Bayes with 85%. It shows that machine learning techniques are effective in exposing cyberbullying, and Bi-LSTM is superior to the other two traditional machine learning classifiers in our study.
An Efficient and Secure Post-Quantum Multi-Authority Ciphertext-Policy Attribute-Based Encryption Method Using Lattice

This article deals with designing an efficient post-quantum lattice based encryption scheme that relies on the multi-authority Ciphertext-Policy Attribute-Based Encryption (CP-ABE). The security of the proposed scheme is based on the hardness of the ring learning with errors (RLWE) problem. The construction of the proposed scheme is done using the Shamir's threshold secret sharing along with the Lagrange interpolation during the key generation and decryption processes in order to achieve the segmentation and restoration of private keys. A comparative study with the existing state of art schemes has been performed to show the feasibility and efficiency of the proposed scheme. Furthermore, experiments on the proposed scheme have been conducted to illustrate the computational time required during the key generation, encryption and decryption processes.
Content-Aware Semantic Communication for Goal-Oriented Wireless Communications

While semantic communications can remarkably reduce the amount of the data transmission traffic without missing critical information, its applications to the efficient goal-oriented wireless communications remain limited. In this paper, we proposed the Content-Aware Semantic Communication (CA-SC) framework, which fuses both global and task-related semantic information via the semantic decoder, thus achieving the goal-oriented wireless communications. In particular, the CA-SC is based on the attention map to locate the task-related semantic information for supporting an adaptive rate allocation scheme. In order to further improve the coding rate as well as reconstructing intended data, we formulate a rate-distortion optimization problem as the loss function for the CA-SC framework, which aims to jointly optimize the semantic codec and the channel codec. Numerical results show that the CA-SC scheme can achieve better performance compared with existing semantic codec scheme and the traditional codec scheme in the image reconstruction task and the object detection task.
Accurate Anomaly Interval Recognition and Fault Classification by Pattern Mining and Clustering

To maintain the stability and reliability of a large-scale information system, monitoring its Key Performance Indicators (KPIs) time series and detecting their anomalies are very important. In practice, however, multivariate time series anomaly detection is challenging due to the large dimension of time series, diverse anomalous patterns, and their complex relationships. In addition, KPIs may exhibit different patterns when different types of faults occur, which aggravates the difficulty of anomaly detection. In this paper, we propose an accurate KPI anomaly detection and fault classification method, which can adapt to multiple metrics and diverse fault types. It can automatically extract common anomalous patterns from different KPI responses when faults occur and accurately determine the fault intervals. In this method, we do not need to deploy a lot of different anomaly detectors, and can conduct both anomaly detection and fault classification simultaneously. Experimental results on the real-world Exathlon benchmark dataset show that our algorithm can accurately recognize the anomaly intervals and classify the faults, with F1-score 0.94.