Reviewerly/exHarmony huggingface dataset:
Introduction from the Guest Editor of Special Issue “Modern Problems of Scientometric Assessment of Publication Activity”

In any branch of intellectual activity that claims to be called the word “science”, there are two approaches to describe the phenomena and objects associated with it; namely, qualitative analysis and quantitative analysis [...]
INTELLIGENT MEASUREMENTS AS A PROMISING WAY TO INTEGRATE AND JOINTLY DEVELOP ARTIFICIAL INTELLIGENCE METHODOLOGIES AND MEASUREMENT THEORY

Modern measurement problems are solved in conditions of uncertainty Significant information uncertainty is caused by the lack of complete and accurate knowledge about the models of measurement objects, influencing factors, measurement conditions, as well as a variety of experimental data. The article briefly discusses the history of the development of methods for the intellectualization of measurement processes, which are also focused on the situation of uncertainty and the classification of measurements and measurement systems. The basic requirements for intelligent measurement systems and technologies and their distinctive properties are formulated. The article discusses the conceptual aspects of intelligent measurements as measurements based on the integration of metrologically certified data and knowledge, and defines intelligent measurements. The properties of intelligent measurements are defined. The article discusses the main properties of soft measurements and their differences from deterministic classical measurements of physical quantities. Cognitive, systemic, and global dimensions are designated as new types of dimensions. In this paper, the methodology and technologies of Bayesian intelligent measurements based on the regularizing Bayesian approach are considered in detail. In conclusion, the main characteristics that should be inherent in intelligent measurement systems, the advantages and prospects of using intelligent measurements both for solving applied problems and for the development and integration of artificial intelligence technologies and measurement theory are formulated.
Trusted QoS Assurance Approach for Composite Service

The characteristics of service computing environment, such as service loose coupling, resource heterogeneity, and protocol independence, propose higher demand for the trustworthiness of service computing systems. Trust provisioning for composite services has become a hot research spot worldwide. In this paper, quality of service (QoS) planning techniques are introduced into service composition-oriented QoS provisioning architecture. By QoS planning, the overall QoS requirement of the composite service is decomposed into separate QoS requirement for every constituent atom service, the QoS level of which can subsequently be satisfied through well-designed service entity selection policies. For any single service entity, its QoS level is variable when the deployment environment or the load of service node changes. To mitigate the uncertainty, we put forward QoS preprocessing algorithms to estimate the future QoS levels of service entities with their history execution data. Then, based on the modeling of composite service and QoS planning, we design three algorithms, which include the time preference algorithm, cost/availability (C/A) preference algorithm, and Euclidean distance preference algorithm, to select suitable atomic services meeting the user’s requirements. Finally, by combining genetic algorithm and local-search algorithm, we propose memetic algorithm to meet the QoS requirements of composite service. The effectiveness of the proposed methods by which the QoS requirements can be satisfied up to 90% is verified through experiments.
Indonesian EFL Learners’ Reading Motivation

Motivation in reading is very important for engaging learners in reading activity which can affect better reading comprehension. However, without motivation in reading can affect the worst reading comprehension. Therefore, motivation in reading needs to be constructed first by the teachers. This study examined whether intrinsic and extrinsic reading motivation different among students and reading motivation in general. This study used a descriptive quantitative approach with a survey design. Eighty-five third-year students voluntarily returned the motivation reading questionnaire about their motivation in reading. The data were analyzed through descriptive statistics using SPSS 23 and criteria-referenced and norm-referenced interpretations. From the analysis, it was found that most extrinsic reading motivation dimension percentages were slightly higher than intrinsic motivation and reading motivation dimension percentages in general in high category percentage. This study concludes that the external reason likely influenced students' motivation in reading. The students who were more motivated to read in this study were because of the expectation to get appreciation from others, good grades, better information from the text, and to finish the assignment. It also implies the theory of motivating students to engage with English reading texts.
Blockchain‐enabled secure crowdsensing for trackside infrastructure information collection and validation in railway signalling data preparation

Rail operators around the world are adopting advanced control systems for railway signalling and train protection to improve their safety performance. In these re-signalling projects, the information about the already installed signals, point machines, track circuits etc. along the trackside infrastructure is essential for the configuration of advanced signalling equipment. The conventional information collection method which is based on unmanned aerial vehicles, helicopters etc. has the access limitations in terrains such as tunnels, hilly regions, river bridges, dense forests etc. To overcome these challenges, this paper proposes the railway vehicular crowdsensing method in which one group of trains participates in this information collection and another group of trains confirms the validity of the collected information. For information exchange between the trains and centralized server, this study chooses the permissioned blockchain-based information transaction mechanism to ensure the trustworthiness. Furthermore, the railway markup language-based blockchain databases are included for information immutability, crowd information integration, and the automatic execution of data preparation signalling rules. Finally, the case studies are carried out to analyse the adequacy of the proposed combination of crowdsensing based secure trackside infrastructure information collection and validation, permissioned blockchain-enabled information transaction and blockchain databases in the proposed signalling data preparation process.
Exploring the Factors that Support Reading Literacy of Elementary School Students in Palu City

Elementary school student literacy is the foundation for developing literacy competencies at the next education level. This study aims to describe the supporting factors for the literacy of elementary school students in Palu City. This research used a qualitative approach with a descriptive design. The data were collected through questionnaire and interview techniques. The collected data were analyzed using descriptive statistical analysis. The results of the data analysis indicated that the supporting factors for reading literacy of elementary school students in Palu City can be classified into three categories, they are (1) students, (2) parents or families, and (3) schools consisting of teachers and infrastructure.
THE EFFECT OF WORD MAP STRATEGY USING INSTAGRAM TO DEVELOP STUDENTS’ VOCABULARY

Study on the use of Instagram has bloomed in EFL teaching learning process. Instagram is one of the favorite SNSs among students. This research aims to discover the effect using Instgram combined with Word Map strategy as learning tool in developing students’ vocabulary by involving 64 Islamic junior high school students. They were asked to do vocabulary level test to measure their mastery in vocabulary. In supporting the data, a questionnaire was also distributed to the treatment group. A quasi-experimental design was employed during the study. A two-way ANOVA was used to analyze the data. The findings revealed that applying Word Map strategy with Instagram significantly improved students’ vocabulary from 11% for the control group to 18.98% for the treatment group. Hence, developing Word Map strategy with mobile application integration to improve students’ writing skill within EFL curriculum is worth investigating.
Formation Of Future Teachers' Professionalism As A Condition Of Career Development

In modern society the requirements for the teacher as a successful person and a professional have significantly increased.It means a qualitatively different level of formation of the motivational sphere and value orientations, professional and pedagogical thinking, technological culture, creative potential and ability for constant self-improvement, adequate career development.Alongside with the positive social attitude towards individual professional career aspirations, some negative trends are observed, such as unrealistically high career ambitions of graduates, employers' skepticism towards unexperienced young teachers.Thus, the professional education is supposed to have the potential to form correct perceptions of professionalism as a prerequisite for the development of young teachers' professional career.It leads to the need for theoretical reflection of modern problems of pedagogical activity and training, based on updated conceptual and methodological foundation with the involvement of the modern conceptual and categorical apparatus, in which such phenomena as "professionalism", "professional success" and "professional career" have recently been approved.These key concepts accurately reflect the peculiarities of the modern stage of human and social development, caused by the transition to a personal and activitybased educational paradigm, changes in the structure of the labour market, the strengthening of personal responsibility for professional integration into society, the complexity of its long-term planning.The article justifies methodological approaches to the process of formation of future teachers' professionalism.The essence and structure of the concept of "teacher's professionalism" and its associated categorical and conceptual apparatus are disclosed.The model of formation of future teachers' professionalism is presented.
Extremal Region Selection for MSER Detection in Food Recognition

The visual analysis of foods on social media by using food recognition algorithm provides valuable insight from the health, cultural and marketing. Food recognition offers a means to automatically recognise foods as well the useful information such as calories and nutritional estimation by using image processing and machine learning technique. The interest points in food image can be detected effectively by using Maximally Stable Extremal Region (MSER). As MSER used global segmentation and many food images have a complex background, there are numerous irrelevant interest points are detected. These interest points are considered as noises that lead to computation burden in the overall recognition process. Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. The performance of ERS algorithm is evaluated based on the classification performance metrics by using classification rate (CR), error rate (ERT), precision (Prec.) and recall (rec.) as well as the number of extremal regions produced by ERS. UECFOOD-100 and UNICT-FD1200 are the two food datasets used to benchmark the proposed algorithm. The results of this research have found that the ERS algorithm by using optimum parameters and thresholds, be able to reduce the number of extremal regions with sustained classification performance.
A Comparative Analysis of Variant Deep Learning Models for COVID-19 Protective Face Mask Detection

The world is in the midst of a paramount pandemic owing to the rapid dissemination of coronavirus disease (COVID-19) brought about by the spread of the virus ‘SARS-CoV-2’. It is mainly transmitted among persons through airborne diffusion of droplets containing the virus produced by an infected person sneezing or coughing without covering their face. The World Health Organization (WHO) has issued numerous guidelines which state that the spread of this disease can be limited by people shielding their faces with protective face masks when in public or in crowded areas. As a precautionary measure, many nations have implemented obligations for face mask usage in public spaces. But manual monitoring of huge crowds in public spaces for face masks is laborious. Hence, this requires the development of an automated face mask detection system using deep learning models and related technologies. The detection system should be viable and deployable in real-time, predicting the result accurately so as to be used by monitoring bodies to ensure that the face mask guidelines are followed by the public thereby preventing the disease transmission. In this paper we aim to perform a comparative analysis of various sophisticated image classifiers based on deep learning, in terms of vital metrics of performance to identify the effective deep learning based model for face mask detection.
Domain-agnostic Document Representation Learning Using Latent Topics and Metadata

Fine-tuning a pre-trained neural language model with a task specific output layer is the de facto approach of late when dealing with document classification. This technique is inadequate when labeled examples are unavailable at training time and when the metadata artifacts in a document must be exploited. We address these challenges by generating document representations that capture both text and metadata in a task agnostic manner. Instead of traditional auto-regressive or auto-encoding based training, our novel self-supervised approach learns a soft-partition of the input space when generating text embeddings by employing a pre-learned topic model distribution as surrogate labels. Our solution also incorporates metadata explicitly rather than just augmenting them with text. The generated document embeddings exhibit compositional characteristics and are directly used by downstream classification tasks to create decision boundaries from a small number of labels, thereby eschewing complicated recognition methods. We demonstrate through extensive evaluation that our proposed cross-model fusion solution outperforms several competitive baselines on multiple domains.
EFFECT OF READING ATTITUDE TOWARD STUDENTS’ READING COMPREHENSION AT THE EIGHTH GRADE OF SMP NEGERI 2 BAUBAU

The objective of this research was to find out the effect of reading attitude toward students’ reading comprehension at the eighth grade of SMP Negeri 2 Baubau. This research used a quantitative approach by applying ex-post facto method of causal comparative design. The population of this research was all the eighth grade students of SMP Negeri 2 Baubau with the number was 320 students. The sample of this research was 30 students who were selected using cluster random sampling technique. The instruments of this research were questionnaire and test. The study results showed that the R score was 0.436 categorized in the moderate category. This means that a positive and moderate correlation existed between the reading attitude and the reading understanding of students. Besides, the score of Fcount was 6.559 with the significant value was 0.016. Because the score Fcount was greater than Ftable (4.18) and the score of significance was lower than α (0.05), it meant that null hypothesis was rejected and alternative hypothesis was accepted. So, based on the finding of research and data analysis, it could be concluded that there was significant influence between reading attitude and students’ reading comprehension at the eighth grade of SMP Negeri 2 Baubau.
Designing an Automatic Agent for Repeated Language–based Persuasion Games

Abstract Persuasion games are fundamental in economics and AI research and serve as the basis for important applications. However, work on this setup assumes communication with stylized messages that do not consist of rich human language. In this paper we consider a repeated sender (expert) – receiver (decision maker) game, where the sender is fully informed about the state of the world and aims to persuade the receiver to accept a deal by sending one of several possible natural language reviews. We design an automatic expert that plays this repeated game, aiming to achieve the maximal payoff. Our expert is implemented within the Monte Carlo Tree Search (MCTS) algorithm, with deep learning models that exploit behavioral and linguistic signals in order to predict the next action of the decision maker, and the future payoff of the expert given the state of the game and a candidate review. We demonstrate the superiority of our expert over strong baselines and its adaptability to different decision makers and potential proposed deals.1
Competence-Based Approach As The Basis Of Modern Education

Using the example of educational programs of the Medical Institute of the Chechen State University, the article examines principles of developing educational programs, analyzes the continuity of competencies in the context of continuous education.The programs for development and modernization of higher professional education determine the need to create an information and educational environment that would contribute to the development of information technology culture and social competences of both teachers and students by stimulating and developing their creative activities.The social order as a personcentered, competence-oriented paradigm of education has set a number of complex tasks for universities: 1) development of a creative personality, ability to self-development and self-improvement;2) development of creative activities and emotional-value relationships with people and oneself;3) development of professional, social, informational competencies; 4) technological readiness of teachers and students to use modern and traditional educational technologies, including information and communication technologies.Higher medical education pays attention to the competence-based approach, since assessment of the effectiveness of training is based on the assessment of knowledge, skills and abilities determined for various medical specializations.Despite the need to reform higher education, the problem lies both in understanding the methodological nature of the competence-based approach and its practical implementation.
Generalized digital lithological map of northern Mexico and southwestern United States of Americaeralized lithological map of northern Mexico and south-southwestern United States of America

The compilation and generalization of the lithological units that crop out in northern Mexico and southwestern United States of America are important to understand the geology and evolution of this part of the North American continent. For this reason, we publish the first interactive digital lithological map with eleven surficial lithological units present in northern Mexico and southwestern United States of America, in which equivalent formations, sequences and igneous units of both countries were merged and generalized. This map shows the distribution of the main types of rocks that evolved in similar geological processes through time, therefore, researchers can use this new compiled map in the analysis and location of samples and geological evolution, or even during studies related to exploration of natural resources. The area covered by the map is comprised between Longitude of 123°10’W and 94°42’W and Latitude of 23°03’N and 36°57’N (Datum WGS-84).
Visual perception of computer-generated stereoscopic pictures: Toward the impact of image resolution

In comparison with the generation of monoscopic images, the time cost of rendering stereoscopic images is doubled. When generating stereoscopic images by computer algorithms, it is desirable to save the computational expense by decreasing the image resolution, without degrading the visual perceptual quality of the images. In this work, to evaluate the perceptual visual quality of computer-generated stereoscopic images (CGSIs), a data set consisting of stereoscopic images created with different horizontal and vertical resolutions was constructed. First, a series of subjective experiments for the analysis of various perceptual situations was conducted. The experimental results show that when the original image resolution was reduced by half, the image difference was not perceptible. In addition, based on full-reference (FR) and no-reference (NR) image quality measurement (IQM), a combined FR-and-NR CGSIQA model was established to predict perceptual quality. We perform weighting calculations for different combinations of FR and NR. The experimental results show that the proposed model significantly outperforms all the classical models.
The Impact of Mobile Technology on Vocabulary Learning of L2 Learners : The Case of WhatsApp Application

Mobile technologies are receiving an increasing attention within diverse educational contexts.However, the scope of research on the affordances various mobile technologies provide to learning outcomes is still limited.The aim of this study was to investigate the effect of using WhatsApp application on vocabulary learning of Iranian EFL learners.Twenty-six female EFL learners at preintermediate level of proficiency were selected through convenience sampling to participate in the study.A vocabulary test was utilized for ensuring the homogeneity of the learners and then the participants were divided into two experimental (N = 13) and control (N = 13) groups, followed by assessing their vocabulary knowledge.Then, the 14-session treatment began, which included teaching new words using WhatsApp in the experimental group and traditional face-to-face method in the control group.At the end of the course, a vocabulary post-test was administered to both groups.Additionally, an attitude questionnaire was administrated to both groups for examining their attitude towards using WhatsApp and the traditional vocabulary learning method.The results indicated that the experimental group outperformed the control group.In addition, it was revealed that the learners who used WhatsApp for enhancing their vocabulary skill had a more positive attitude toward foreign language vocabulary learning as compared to the other group.The implications of the study for mobile-assisted vocabulary learning are discussed.
Evaluasi Pelaksanaan Identifikasi Pasien Dalam Pemberian Terapi, Transfusi, Pemeriksaan Penunjang Terhadap Insiden Dirumah Sakit

Introduction Automatic identification provides an efficient way to prevent errors that result in incidents in patient safety. Many studies have shown that many errors in patient identification occur in the administration of drugs, transfusions, and investigations The purpose of reviewing articles is to see the evaluation of patient evaluations in providing therapy and transfusions, supporting examinations of the impact in the hospital. Method The research design used was a literature review study with a methodological review. Strategies in finding journals used PICO and Articles selected using a systematic review and meta-analysis of PRISMA flow models (McInnes et al., 2018). in the initial stages of searching with an online database, EBSCHO, PUBMED, Google Scholar, articles and fortal online. selection of articles based on inclusion criteria, and based on keywords as a whole resulted in a total of 7articles.Results After a comprehensive synthesis of 7 articles describing the importance of patient identification when taking action on patients by nurses, doctors or other health professionals who have an impact on patient safety