Reviewerly/exHarmony huggingface dataset:
The impact of digital interaction on the transformations of socio-economic and educational processes in Russian society

In contemporary Russian society, the development of digitalization is tied with the battle against the detrimental effects of COVID-19. In the face of the spread of the infection transmitted primarily through direct human interaction, the need to change the format of this interaction to digital has risen. The dynamics of digitalization were boosted, leading to several problems. There surfaced the issues of technical difficulties, the lack of digital literacy on the part of individual staff members in organizations and some social groups and individuals (primarily those in the socially vulnerable population groups), the limitations of direct interpersonal interaction, the redundancy of digital information, emotional fatigue from having to work and learn only remotely, the cybersecurity of online interactions, a significant increase in digital control over citizens’ lives, the risks of social and psychological tension and distrust of digitalization, technophobia, inability to rationally apply the successes of scientific and technological advancement to improve personal and social well-being.
The gender reason for the readiness of future psychologists to implement a professional career

Being a social being, a person fulfills many social roles - in the family, in the work team, in the circle of friends, in the social environment (transportation, service sector, education, etc.). And these roles do not always correspond to the ideas formed in society regarding the functional purpose of men and women. Purpose - theoretical substantiation of the influence of gender identity on the readiness of future psychologists to pursue a professional career.&#x0D; Taking into account the gender characteristics of those acquiring the profession of psychologist indicates that the teacher acts with an understanding of the socio-cultural nature of the origin of the concepts of "masculinity" and "femininity", considers the personality of the future specialist above traditional gender norms and gender stereotypes, if they to some extent restrain and slow down the formation of psychological readiness for professional activity and the realization of a career. Understanding the gender-specific features of education is necessary not only for teachers to choose adequate methods and technologies, but also for the students of higher education themselves for self-improvement and self-development.&#x0D; Studying the problems of a professional career, modern researchers note that both men and women are interested in career growth (vertical career) and increasing their educational level and professionalism (horizontal career). People of both sexes are prestige-oriented, concerned about their positive professional image, decent pay, and have a high sense of responsibility. However, there is also a significant difference in the level of professional career success of women and men.
A Joint Operation Simulation Environment for Reinforcement Learning

Reinforcement learning has received broad attention from multiple areas due to its remarkable successes nowadays. And the intelligence about decision making is becoming the new frontier of artificial intelligence. Among various real-world scenarios need accurate decision making, military decisions, however, been studied by few people. This paper describes a reinforcement learning environment powered by a war game, which is considered as a high level simulation for military operation. We define the observation and action space for this environment, along with a system designed for programmatic access. We also provide a series of mini-games for evaluation.
Rancang Bangun Sistem Pendukung Keputusan Pemilihan Sales Terbaik pada PT Caturadiluhur Sentosa Menggunakan Metode SAW

&#x0D; PT Caturadiluhur Sentosa Palembang is a company engaged in the distribution, logistics, and retail of building materials. PT Caturadiluhur Sentosa Palembang has several obstacles, namely the criteria for determining the best current sales by determining the largest turnover only. So that the current best sales appraisal system is considered less effective and unfair. The system built aims to build the best sales decision support system that can manage sales data and assessment parameters properly. The system built can manage sales data, criteria data, store sales appraisal data, and calculate sales value using the SAW method. The system is built using the iteration method. The result of this research is a decision support system that gives the best sales ranking based on the results of the assessment calculated using the SAW method.&#x0D; Keywords: Decision Support System, Iteration, SAW
Analisis Efektivitas Penerapan Model Make A Match Berbasis Media Puzzle Terhadap Hasil Belajar Siswa SD pada Mata Pelajaran IPS di SD

This research is motivated by the learning model used by the teacher is still using the old learning model. Thus, to make it easier for students to receive subject matter, especially in social studies subjects, a make a match model is applied in learning. When learning material is difficult for students to accept, it will affect student learning outcomes. Based on the results of the study, the application of the make a match learning model can improve student learning outcomes because the model is easy for students to understand and also fun for students.
LUNG CANCER DETECTION BY HYBRID LEARNING METHOD APPLYING SMOTE TECHNIQUE

Lung cancer is a very deadly disease. However, early diagnosis and detection is an essential factor in overcoming this deadly disease. Tumors formed in this disease's initial stage are divided into benign and malignant. These can be visualized using a computed tomography (CT) scan. Thanks to machine learning and deep learning, cancer stages can be detected using these images. In our study, the best and most promising results in the literature were obtained by using a hybrid learning architecture. The data mining techniques we use in obtaining these results also play a significant role. The best accuracy result we obtained belongs to the CNN+GBC hybrid algorithm, which we recommend with 99.71%.
Attentive Sequential Auto-Encoding Towards Unsupervised Object-centric Scene Modeling

This paper describes an unsupervised sequential auto-encoding model targeting multi-object scenes. The proposed model uses an attention-based formulation, with reconstruction-driven losses. The main model relies on iteratively writing regions onto a canvas, in a differentiable manner. To enforce attention to objects and/or parts, the model uses a convolutional localization network, a region level bottleneck auto-encoder and a loss term that encourages reconstruction within a limited number of iterations. An extended version of the model incorporates a background modeling component that aims at handling scenes with complex backgrounds. The model is evaluated on two separate datasets: a synthetic dataset that is constructed by composing MNIST digit instances together, and the MS-COCO dataset. The model achieves high reconstruction ability on MNIST based scenes. The extended model shows promising results on the complex and challenging MS-COCO scenes.
Analysis on AHP-VIKOR-based Supplier Selection for Large Equipment Maintenance of Construction Enterprises

Since the outburst of COVID-19, the medical system has been facing great challenges due to the explosive growth in detection and treatment needs within a short period.To improve the working efficiency of doctors, an improved EfficientNet model of Convolutional Neural Network (CNN) was proposed and applied for the diagnosis of pneumonia cases and the classification of relevant images in the present study.First, the acquired images of pneumonia cases were divided to determine the zones with target features, and image size was limited to improve the training speed of the network.Meanwhile, reinforcement learning was performed to the input dataset to further improve the training effect of the model.Second, the preprocessed images were inputted into the improved EfficientNet-B4 model.The depth and width of the model, as well as the resolution of the input images, were determined by optimizing the combination coefficient.On this basis, the model was scaled, and then its ability in extracting the features of deep-layer images was strengthened by introducing the attention mechanism.Third, the learning rate was adjusted by using the Adaptive Momentum (ADAM), and the training efficiency of the model was accelerated.Finally, the test set was inputted into the trained model.Results demonstrate that the improved model could detect 98% of patients with pneumonia and 97% of patients without pneumonia.The accuracy rate, precision rate, and sensitivity of the model were generally improved.Lastly, the training and test results of VGGNet, SqueezeNet-Elus, SqueezeNet-Relu, and the improved EfficientNet-B4 models were compared and evaluated.The improved EfficientNet-B4 model achieved the highest comprehensive accuracy rate, reaching 92.95%.The proposed method provides some references to the application of the CNN model in image classification and recognition.
The Challenges of E-Learning for Higher Education Lecturers and Learners

The use of E-Learning in the English language learning process is very useful for stakeholders, especially lecturers and learners. But in reality, lecturers and learners often experience difficulties in using it both from a technical and non-technical perspective. This study aims to investigate the challenges faced by EFL lecturers and learners and how they face them. To overcome this problem, this research employed a case study. This study involved 244 learners and 12 lecturers to fill out a questionnaire and five of them were interviewed. To collect questionnaire data, this research used a google form, meanwhile, the researcher carried it out online using the zoom application for interviewing respondents. After collecting the data, the results of the questionnaire were processed using IBM SPSS software and the interview results were described qualitatively. The results showed that lecturer constraints include lack of time to develop E-Learning content, inability to optimally utilize E-Learning features, problems in lecturer-learner interaction, poor internet connection, and lack of learner motivation. Meanwhile, the challenges of learners include the lack of tools to access E-Learning, lack of motivation, technical problems, and barriers to E-Learning features. It can be concluded that lecturers and learners face challenges both technically and non-technically in using E-Learning. The implication of this research is that despite all the challenges faced, lecturers and learners are required to be able to adapt to E-Learning environment in order to facilitate the online learning process.
Cyber Incident Handling Framework for Schools in South Africa: Views of Experts

The goal of this study was to create a Cyber Incident Handling Framework (CIHF) for South African schools to improve their effectiveness in dealing with cyber incidents while also ensuring that each role player plays an important role in the intervention process aimed at reducing cyber incidents in schools. Experts' comments on the proposed cyber incident handling framework (CIHF) for schools in South Africa were gathered through an expert review survey. The use of expert reviewers was justified to tap into the reviewers' expertise and knowledge. The expert opinion was useful in determining the relevance of the suggested framework, reflecting on its quality, and determining how well it supports the solution of reporting cyber events in South African schools. The expert review process' significant contribution was the documentation of expert opinions on the planned CIHF for South African schools. The study detailed the Cyber Incident Handling Framework (CIHF), as well as the problems in applying the framework and how the challenges may be handled.
Central Bank Digital Currency: A New Instrument of Monetary Policy

Central bank digital currency is a claim on the central bank, which is accessible by a wide range of customers, digital, flexible and potentially interest-bearing. The new asset is being researched in many countries, and widening the accessibility of financial services, creating the digitalized form of cash with monetary policy considerations being among its motivations. An interest-bearing central bank digital currency could make the central bank’s monetary transmission direct, which could improve the efficiency of monetary policy. Furthermore, the asset could help to strengthen competition among banks and could support the spread of financial innovations. Risks related to the instrument, such as disintermediation, can be counterbalanced by the increasing deposit volumes due to higher interest rates after the introduction of the instrument, and by limits set on the maximum holdings of the asset. An interest-bearing central bank digital currency can make targeted monetary policy possible, furthermore, the direct transmission of interest rates could enhance the efficiency of monetary transmission.
Communication Quality Indicator System of Industrial Communication Network and Related Diagnostic Evaluation

The industrial communication networks are the infrastructures to connect the bottom field devices and control devices in the factory, which realize the interconnection and interoperability of intelligent manufacturing. Its communication quality is a prerequisite to ensure the normal operation and production in the factory. According to the layered functions of industrial communication network protocol, this paper establishes a communication quality indicator system, consisting of physical layer waveform characteristic indicators and time characteristic indicators, data link layer performance indicators and function indicators, and application layer behavior related indicators. The general process of industrial field communication quality diagnosis and evaluation is proposed. Through the detection of industrial communication quality indicator parameters, the possible problems in the field can be diagnosed and evaluated, thus the rapid location of communication anomalies or faults in the industrial field can be achieved.
Meningkatkan Kemampuan Membaca Permulaan melalui Kegitan Bermain Kartu Huruf Bergamabar pada Kelompok B TK Harapan Kelayu Kecamatan Selong Tahun Pelajaran 2020/2021

The objectives of this classroom action research (CAR) are. knowing the improvement of early reading skills through picture card playing activities in group B. Early Childhood Education TK Harapan Kelayu, Selong District 2016/2017. In this class action research (CAR) was carried out in 2 cycles, from the results of the actions taken it was proven to be able to increase student learning mastery classically, namely in the first cycle by 60%, it could increase to 100% in cycle II. The results of this classroom action research show that learning by applying pictorial letter cards can improve the quality of learning and student learning mastery in language skills subjects with completeness reaching 100%.
An Extensible Heterogeneous Network Embedding Framework for Knowledge Tracing

PDF HTML XML Export Cite reminder An Extensible Heterogeneous Network Embedding Framework for Knowledge Tracing DOI: 10.21655/ijsi.1673-7288.00292 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Knowledge tracing is of great significance for providing better personalized learning guidance and has thus attracted extensive research attention in recent years. The task of knowledge tracing is to model students' learning process on the basis of historical exercise records and trace students' knowledge proficiency, thereby predicting students' performance on future exercises or recommending exercises for better proficiency. Existing methods focus on either the skill level or the exercise level, ignoring the relationships among exercises and Knowledge Components (KCs). The classical single-factor models include the Deep Knowledge Tracing (DKT) {model} and the Dynamic Key-Value Memory Network (DKVMN) model. Although a few models, such as the Bayesian Knowledge Tracing (BKT) model and the Knowledge Proficiency Tracing (KPT) model, utilize the Q-matrix to improve model performance, most of them ignore the interaction among KCs, not to mention models that do not use the Q-matrix. Inspired by the recent success of network embedding, this paper presents a heterogeneous network embedding framework for knowledge tracing called HNEKT that takes both exercises and KCs into account. To adapt to the application of knowledge tracing, this paper also proposes several meta-paths to generate meaningful node sequences for network embedding. Besides, it explores other side information as well to improve the extensibility and effectiveness of the proposed model. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HNEKT model. Reference Related Cited by
KubeCOM: an implementation of a non-containerized software management system based on Kubernetes

The unified deployment and management of non-containerized software is the prerequisite and key to achieve efficient management of cloud computing platforms. With the rapid development of information technology, Kubernetes technology is widely employed in container orchestration and cluster deployment. As a novel generation of containerized application cluster platform, Kubernetes has the advantages of automatic resource scheduling mechanism and elastic scaleup in a single cloud platform. With the development of hybrid clouds such as private cloud, government cloud, and local cloud, traditional non-containerized software management methods are stretched to the limit. In this paper, we present a declarative non-containerized software management approach based on Kubernetes and elaborates the system management and deployment software in detail. The management system takes full use of the advantages of the Kubernetes platform, and combines the characteristics of automatic management, Operator and CRD technology, etc., to provide a stable, reliable and powerful guarantee for the automatic deployment and management of hybrid cloud platform resources.
Radar signal recognition method based on SVM model

Radar signal recognition is a key step of electronic reconnaissance. In order to use various parameters of radar signal to identify its country in complex electromagnetic environment, this paper analyses the characteristics of radar signal and the development status of related recognition methods. Aiming at the support vector machine (SVM) classification algorithm in machine learning, this paper studies the feature selection and the establishment, evaluation and optimization of model, and completes the programming and application of the algorithm, the recognition accuracy and efficiency are improved. The research shows that when SVM is applied to radar signal recognition, the classification accuracy is more than 93% and the AUC value is more than 0.99.
Artificial intelligence applications in medical sciences: illustrations in pharmaceutical and medical imaging areas

Artificial intelligence (AI) technologies have been used to develop and advance healthcare industry. Typical application scenarios in medical sciences include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient, transcribing medical records, and remotely treating patients. This paper aims at introducing AI applications in the field of medical sciences, especially the areas of pharmaceutical and medical imaging. It first introduces the current AI technologies used in medical science in general, and then showcases these applications in pharmaceutical and medical imaging areas. Insilico Medicine (Hong Kong) and Pfizer (US) are the illustration cases from pharmaceutical industry; and Deepwise (China) and GE healthcare (US) are selected cases from medical imaging industry. The comparison within each industry and between two countries (China and US) are summarized at the end.
Wayang as a Media for Spreading Islam in the Archipelago

This article contains a discussion of the history of the spread of Islam through wayang art that developed in Nusantara. The writing of this article aims to explain the early arrival of Islam to Nusantara, the origin of wayang as a medium for the spread of Islam, to the role of wayang in the spread of Islam. In the preparation of this article, the author uses the literature study method to find and collect various relevant sources. Regarding wayang art, there are various Islamic religious values that can be found from wayang. The use of wayang as a medium for spreading Islam is thought to be inseparable from the initial position of wayang as an artistic performance used to spread religious, philosophical, and ethical teachings to humans. In addition, at that time wayang were also very popular with the public. The role of wayang in the spread of Islam is so important and this cannot be separated from the role of Wali Songo. Among them there are Sunan Giri, Sunan Kalijaga, and Sunan Bonang who preach using modified wayang as their medium. Both in terms of form and wayang stories have been adapted to Islamic teachings. When wayang has been adapted to Islamic teachings, people continue to like the art of wayang performed and can accept Islamic teachings easily. Keywords: Spread of Islam, Wali Songo, Wayang.
TOURIST SATISFACTION WITH THE QR CODES ON STREET ART IN SONGKHLA OLD TOWN, THAILAND

Quick Response code or QR code is a type of bar code that is used widely in many organizations including in tourism to provide various information. This study aims to investigate tourists’ expectations of information provided by QR codes and to study tourists’ satisfaction with QR codes on Street Art in Songkhla old town, Thailand. Quantitative research design was employed in this study. 100 Thai and non-Thai respondents from each of the two phases of data collection answered each set of two questionnaires. Majority of the tourists responded for the need of QR codes to represent Street Art paintings in Songkhla old town as they would like to know the meaning of the paintings in more detail. The language used in QR codes should be English and Thai. The majority of the tourists showed satisfaction with QR code as information sources for Street Art paintings in terms of design, codes’ easy usage, and its accessibility which is not complicated. The results of this study can facilitate travelers’ rapid and easy access to tourism information. The QR codes can improve tourists’ experience by giving background information behind each painting related to history, culture and local ways of life. It helps both tourists and locals to discover more information about the cultural heritage they inherited and to enhance their awareness of heritage preservation. Tourism stakeholders and policy makers also benefit from this study as the information can be used to develop the QR codes for other tourist destinations. Additionally, street art plays an important role in creating creativity in the city and contributes in promoting Songkhla Old Town. It is considered as a tool to drive the local economy.