Reviewerly/exHarmony huggingface dataset:
Professional development of kazakhstani rural teachers of russian language and literature in the context of digitalization of education

Introduction. The professional development of a teacher improves the quality of education, contributes to the improvement of the teacher's skills and motivates him for further continuous professional growth. The purpose of the article is to diagnose the professional needs of Kazakhstani rural teachers of Russian language and literature and to determine the factors contributing to their professional development. Research methods. To determine the assessment of the activities, working conditions and professional development of Kazakhstani teachers, the results of the international study of the teaching staff on teaching and learning TALIS-2018 were analyzed. The method of secondary data analysis based on a large-scale study was used, as a result of which it has high validity. The SPSS statistical program was used to process an international database. In order to determine the needs of rural teachers and factors that negatively or favorably affect their professional development, a questionnaire was conducted. More than 70 rural teachers of Russian language and literature of Almaty region took part in the online survey. In addition, focus groups with teachers of the Almaty region were conducted to obtain high-quality data. The interview was processed using the decryption method, that is, transcribing audio recordings and encoding texts. Results. The article analyzes how the data of the international TALIS-2018 study were used to reform Kazakhstan's educational policy. In addition, the results of the online survey allowed the authors to conclude that rural teachers of Russian language and literature prefer to master the topics of digital technologies in education (54.5%), functional literacy and critical thinking (54.3%), assessment methods (47.1%). The professional needs of rural teachers are associated with the need to improve teaching methods (41.2%), consult with the most experienced colleagues (32.5%) and develop skills to work with children with special educational needs. Conclusion. The directions contributing to the professional growth of rural teachers of Russian language and literature and the quality of improving the academic performance of students in rural schools where there is no language environment and poor quality of Internet connection are identified: the formation of digital competencies of rural teachers, extracurricular work, mentoring and professional support, diagnostics of professional needs of rural teachers and the organization of advanced training courses, methodological support of small schools.
Socialization Of The Digital Generation Of Students In A Transforming Society

The authors discuss the specifics of the digital generation of university students as a special social group that exists in mixed reality and hyperconnection to the Internet, social networks and instant messengers.It is found that the digital generation of students is in rather complex and contradictory conditions of modification of traditional socialization in the context of updating norms, samples, attitudes and behavioral models mediated by the virtual environment, mastering the new sociality in consumption and the formation of a new culture of knowledge and leisure and social and interpersonal communications in the unity of reality and virtuality.The paper presents the results of an empirical study carried out within the framework of a quantitative-qualitative methodology in order to identify the riskiness of the socialization of the digital generation of university students.The analytical assessment of the most massive responses of students to the understanding of emerging risk situations during the use of the Internet, social networks, instant messengers in education, quasi-professional and social activities is given.The risks of the time interval of the use of various digital equipment by students are presented.
Spatiotemporal image quality of virtual reality head mounted displays

Virtual reality (VR) head mounted displays (HMDs) require both high spatial resolution and fast temporal response. However, methods to quantify the VR image quality in the spatiotemporal domain when motion exists are not yet standardized. In this study, we characterize the spatiotemporal capabilities of three VR devices: the HTC VIVE, VIVE Pro, and VIVE Pro 2 during smooth pursuit. A spatiotemporal model for VR HMDs is presented using measured spatial and temporal characteristics. Among the three evaluated headsets, the VIVE Pro 2 improves the display temporal performance using a fast 120 Hz refresh rate and pulsed emission with a small duty cycle of 5%. In combination with a high pixel resolution beyond 2 k [Formula: see text] 2 k per eye, the VIVE Pro 2 achieves an improved spatiotemporal performance compared to the VIVE and VIVE Pro in the high spatial frequency range above 8 cycles per degree during smooth pursuit. The result demonstrates that reducing the display emission duty cycle to less than 20% is beneficial to mitigate motion blur in VR HMDs. Frame rate reduction (e.g., to below 60 Hz) of the input signal compared to the display refresh rate of 120 Hz yields replicated shadow images that can affect the image quality under motion. This work supports the regulatory science research efforts in development of testing methods to characterize the spatiotemporal performance of VR devices for medical use.
Lightweight Semantic-Guided Neural Networks Based on Single Head Attention for Action Recognition

Skeleton-based action recognition can achieve a relatively high performance by transforming the human skeleton structure in an image into a graph and applying action recognition based on structural changes in the body. Among the many graph convolutional network (GCN) approaches used in skeleton-based action recognition, semantic-guided neural networks (SGNs) are fast action recognition algorithms that hierarchically learn spatial and temporal features by applying a GCN. However, because an SGN focuses on global feature learning rather than local feature learning owing to the structural characteristics, there is a limit to an action recognition in which the dependency between neighbouring nodes is important. To solve these problems and simultaneously achieve a real-time action recognition in low-end devices, in this study, a single head attention (SHA) that can overcome the limitations of an SGN is proposed, and a new SGN-SHA model that combines SHA with an SGN is presented. In experiments on various action recognition benchmark datasets, the proposed SGN-SHA model significantly reduced the computational complexity while exhibiting a performance similar to that of an existing SGN and other state-of-the-art methods.
Public earthquake communication in Italy through a multi-source social media platform: The INGVterremoti experience (2010–2022)

Communicating scientific information about earthquakes is an important and delicate issue in countries like Italy, where seismic risk is high. Furthermore, continuous and scientifically sound communication is needed, especially in recent times when social media have amplified the risk of being biased by misinformation, fake news and conspiracy theories. For this reason, we have developed a communication strategy for earthquake science and risk in Italy, mostly based on social media. The INGVterremoti platform was born between 2010 and 2012 with the goal of increasing scientific information released to the public, and also establishing a two-way communication channel between scientists and citizens. In the past 12 years, the INGVterremoti platform has gained trust and popularity, increasing the number of involved people, which amounts today to several hundred thousand. The platform consists of a coordinated suite of social media channels and a blog-magazine, where updates on ongoing earthquake sequences and posts on scientific topics are continuously published. Our end users are mostly citizens, but also authorities and media. Special attention has been given to interactions with the public, especially on our Facebook page, in order to understand their information needs, identify rumors and fake news, particularly in areas affected by seismic sequences, and address the most pressing requests. In this paper we describe the INGVterremoti strategy, the different media that we use, focusing on their strengths and weaknesses. We concentrate on the experience, carried out in the last few years, of the publication of provisional information on ongoing earthquakes, a long-standing issue strongly requested by our followers. The INGVterremoti platform has played a fundamental role in many seismic sequences of the past 12 years in Italy, starting from the Emilia sequence in 2012, to the central Italy one, started with the deadly earthquake of 24 August 2016 and still ongoing. Besides the periods of high attention after strong earthquakes, we used the INGVterremoti social media as a tool for releasing continuous and sound information to the public, and as a way to involve citizens in the communication arena.
Inhibitors and Enablers to Explainable AI Success: A Systematic Examination of Explanation Complexity and Individual Characteristics

With the increasing adaptability and complexity of advisory artificial intelligence (AI)-based agents, the topics of explainable AI and human-centered AI are moving close together. Variations in the explanation itself have been widely studied, with some contradictory results. These could be due to users’ individual differences, which have rarely been systematically studied regarding their inhibiting or enabling effect on the fulfillment of explanation objectives (such as trust, understanding, or workload). This paper aims to shed light on the significance of human dimensions (gender, age, trust disposition, need for cognition, affinity for technology, self-efficacy, attitudes, and mind attribution) as well as their interplay with different explanation modes (no, simple, or complex explanation). Participants played the game Deal or No Deal while interacting with an AI-based agent. The agent gave advice to the participants on whether they should accept or reject the deals offered to them. As expected, giving an explanation had a positive influence on the explanation objectives. However, the users’ individual characteristics particularly reinforced the fulfillment of the objectives. The strongest predictor of objective fulfillment was the degree of attribution of human characteristics. The more human characteristics were attributed, the more trust was placed in the agent, advice was more likely to be accepted and understood, and important needs were satisfied during the interaction. Thus, the current work contributes to a better understanding of the design of explanations of an AI-based agent system that takes into account individual characteristics and meets the demand for both explainable and human-centered agent systems.
Construction of Combined Teaching Evaluation System Based on STATA Analysis

Designing a reasonable combined teaching scheme is not only the need to achieve teaching optimization, but also a reasonable choice to improve the overall level of teaching efficiency. The existing researches lack an evaluation system to judge the rationality of their designs. Therefore, this paper takes music teaching as an example to carry out the research on the construction of combinatorial teaching evaluation system based on STATA analysis. Firstly, the implementation process of the combined teaching strategy is provided here, and the construction of the combined teaching evaluation system is carried out from seven aspects, namely teaching in fun, emotional experience, student engagement, elicitation and induction, teaching according to students’ aptitude, creative development and vividness and intuitiveness. The entropy weight method is adopted to assign the weight of the combined teaching evaluation index of music major courses. Based on stata15.1 software tool, regression analysis is carried out on the main factors that affect the effect of combined teaching, and the relevant regression analysis model is constructed to realize the effect prediction of short-term combined teaching scheme in the case of small samples. The experimental results verify the effectiveness of the evaluation system and analysis model.
Regularized impurity reduction: accurate decision trees with complexity guarantees

Abstract Decision trees are popular classification models, providing high accuracy and intuitive explanations. However, as the tree size grows the model interpretability deteriorates. Traditional tree-induction algorithms, such as C4.5 and CART, rely on impurity-reduction functions that promote the discriminative power of each split. Thus, although these traditional methods are accurate in practice, there has been no theoretical guarantee that they will produce small trees. In this paper, we justify the use of a general family of impurity functions, including the popular functions of entropy and Gini-index, in scenarios where small trees are desirable, by showing that a simple enhancement can equip them with complexity guarantees. We consider a general setting, where objects to be classified are drawn from an arbitrary probability distribution, classification can be binary or multi-class, and splitting tests are associated with non-uniform costs. As a measure of tree complexity, we adopt the expected cost to classify an object drawn from the input distribution, which, in the uniform-cost case, is the expected number of tests. We propose a tree-induction algorithm that gives a logarithmic approximation guarantee on the tree complexity. This approximation factor is tight up to a constant factor under mild assumptions. The algorithm recursively selects a test that maximizes a greedy criterion defined as a weighted sum of three components. The first two components encourage the selection of tests that improve the balance and the cost-efficiency of the tree, respectively, while the third impurity-reduction component encourages the selection of more discriminative tests. As shown in our empirical evaluation, compared to the original heuristics, the enhanced algorithms strike an excellent balance between predictive accuracy and tree complexity.
Special Issue on Requirements Engineering, Practice and Research

With recent developments in cloud and mobile computing technologies, the growing need for secure, trustworthy, and cost-efficient software and the shortage of highly skilled professional software developers have given rise to a new generation of problems that require improved forms of specification and representation of such systems at multiple abstraction levels, with various concerns and stakeholder perspectives [...]
Object Detection Based on the GrabCut Method for Automatic Mask Generation

The Mask R-CNN-based object detection method is typically very time-consuming and laborious since it involves obtaining the required target object masks during training. Therefore, in order to automatically generate the image mask, we propose a GrabCut-based automated mask generation method for object detection. The proposed method consists of two stages. The first stage is based on GrabCut's interactive image segmentation method to generate the mask. The second stage is based on the object detection network of Mask R-CNN, which uses the mask from the previous stage together with the original input image and the associated label information for training. The Mask R-CNN model then automatically detects the relevant objects during testing. During experimentation with three objects from the Berkeley Instance Recognition Dataset, this method achieved a mean of average precision (mAP) value of over 95% for segmentation. The proposed method is simple and highly efficient in obtaining the mask of a segmented target object.
The Use of Smartphones in Geography Learning: A 21st Century Learning Innovation in Identifying Nature Appearances Based on Fieldwork

Geography learning process can be done both indoor and outdoor. Geography learning can provide new findings/ concepts from a natural phenomenon which exists in the field. In order to build an understanding, concept, and even findings; geographer can utilize fieldwork-based geography learning. It is necessary to have a framework that explains the existence of the study location. The purpose of this study is to give space or scope for geography learning which emphasizes fieldwork-based learning in natural laboratories using a smartphone in data collection. The method used is a field survey conducted by determining the location points that will be used as access in field-based geography learning, with a landscape and social approach. After that, it is processed to get routes and learning areas that are ready with smartphones. Furthermore, it is applied to students to carry out field-based learning using smartphones. The result of this study shows some areas that can be used as fieldwork by using a smartphone during a field survey. There are 15 spots recorded on smartphone that can be used as geography learning fieldwork. Learners can do observation, measurements, interview about natural and social landscape in these areas. Fieldwork-based learning in Sumbermanjing Wetan has its estimation in leaning based on the aims. Besides, things like funding, licensing, and the security of the learning areas should be paid attention, in order to reach the learning outcome. It is also need a preliminary survey or observation to be able to estimate fieldwork leaning areas in Sumbermanjing Wetan district.
Sentiment Classification of Hindi Language using Natural Language Processing Techniques

This paper has presented Hybrid Approach for determination of sentimental phrase or words from Hindi text automatically through use of Hindi sentiment’s lexicon and classifying them into polarity i.e. Positive, Negative and Neutral.
A Study on the Prospect of the Cloud Kitchen Model in Dhaka

This study looks at the prospect of the Cloud Kitchen Model (CKM) in Dhaka, Bangladesh. Cloud kitchen is a centralized commercial food production facility with no dine-in facility. The study first identified the existing perceptions and expectations of the critical stakeholders for whom the CKM can potentially bring competitive advantages over traditional brick-and-mortar-based models. The target audiences for this study are consumers of online food delivery systems, traditional restaurant owners, potential CKM restaurant owners, and industry experts. To perform this mix-method based study, the researchers conducted surveys along with Focused Group Discussions and In-depth Interviews targeted to various stakeholders such as consumers, restaurant owners, and industry experts besides utilizing secondary data sources. Most respondents know about cloud kitchens, and primarily most of them heard from social media, but mere knowledge of the CKM does not influence their ordered food preference. The only time consumers showed a greater preference for CKM over traditional brick-and-mortar models is when the comparison comes mostly in terms of price, quality, and packaging. As per the results of this study, for the restaurant ecosystem of Dhaka, CKM offers a narrow sales prospect for the next three years even though it offers Value Added Tax (VAT) benefits in general. Existing restaurant owners perceived immediate additional capital requirements, other than their available capital in hand for running their existing restaurants at the moment, as a barrier towards entering the cloud kitchen business and thus stopping the adoption of CKM in the next three years even though they are interested to initiate CKM. It is recommended that those who want to start a CKM business should proceed with a robust business model and can look for angel investors or venture capitalists to secure funds and add experts to the business while prioritizing their marketing activities. CKM-based restaurants should utilize social media platforms to build a positive brand image. Finally, CKM-based restaurants should focus on consumer needs through customized platforms (e.g., their own delivery media and websites) and emphasize employee retention through proper employee training and benefits.
Feature Selection Using Neighborhood based Entropy

Feature selection plays an important role as a preprocessing step for pattern recognition and machine learning. The goal of feature selection is to determine an optimal subset of relevant features out of a large number of features. The neighborhood discrimination index (NDI) is one of the newest and the most efficient measures to determine distinguishing ability of a feature subset. NDI is computed based on a neighborhood radius ( E ). Due to the significant impact of E on NDI, selecting an appropriate value of E for each data set might be challenging and very time-consuming. This paper proposes a new approach based on targEt PointS To computE neIgh- borhood relatioNs (EPSTEIN). At first, all the data points are sorted in the descending order of their density. Then, the highest density data points are selected as many as the number of classes. To determine the neighborhood relations, the circles centered on the target points are drawn and the points inside or on the circles are considered to be neighbors. In the next step, the significance of each feature is computed and a greedy algorithm selects appropriate features. The performance of the proposed approach is compared to both the commonest and newest methods of feature selection. The experimental results show that EPSTEIN could select more efficient subsets of features and improve the prediction accuracy of classifiers in comparison to the other state-of-the-art methods such as Correlation-based Feature Selection (CFS), Fast Correlation-Based Filter (FCBF), Heuris- tic Algorithm Based on Neighborhood Discrimination Index (HANDI), Ranking Based Feature Inclusion for Optimal Feature Subset (KNFI), Ranking Based Feature Elimination (KNFE) and Principal Component Analysis and Information Gain (PCA-IG).
Media Education as an Effective Perspective on the Formation of Ideology in Society Through the Influence of Self-Consciousness

The study identifies the trends of media education in the post-industrial period, marked by the development of information and communication technologies in the integration and globalization processes. Society needs competent specialists who develop and strive for self-realization in society. The educational process is a phenomenon that covers different spheres of human activity and determines the main aspects of the regulatory and legal framework. The article illustrates innovative styles of media education that are an effective perspective on the formation of ideology in society through the influence of self-consciousness in educational activities. In order to identify the main aspects of media technology in the educational process, we investigated the basic methodological and theoretical foundations based on the generalizations of researchers and scientists. The meaning of the concept of media education and media technology was studied, the evolution of education from the introduction of information and communication technologies in the learning process to the formation of critical thinking and analysis of the information space was investigated. The method of synthesis, analysis was used to define the main concepts of the study. Descriptive method, research and scientific method was used to compile and summarize the results of the implementation of media education as a prospect for the formation of ideology in society through the influence of self-consciousness.