Reviewerly/exHarmony huggingface dataset:
On the question of the new architecture of the Russian educational space

Introduction . After leaving the Bologna system, the Russian education system faced many new challenges. Most of them are associated with the carelessness of developing new, relatively autonomous algorithms for the transformation of the Russian educational system. Purpose setting . The article considers a set of factors determining the creation of such algorithms. Methodology and methods of the study . The methodology of the analysis of such algorithms is based on a predictive analysis of the prospects for the development of the Russian educational space, presented by Russian and foreign trendwatchers. The models of the future world economy focused on the processes of transformation of the education system. The object of research is limited to the sphere of higher professional education. The object of research is limited to the sphere of higher professional education. Results . Usually global education is explicated as «filling» national education systems with global knowledge, meanings and values. By and large, the latter were the methodological foundations of liberal models, similar to the models of R. Henvey and M. Botkin. The world as a whole, humanity as a global community, humanism, holism made up the qualitative features of liberal education in its global discourse. But the withdrawal from the Bologna system, the change in the economic and socio-political situation raised the question of forming a new view on the problems of globalization of education for Russian regulators. The output itself can be viewed in two ways. On the one hand, as an unsuccessful finale of the integration of the Russian educational space into the European (and in parallel into the world) educational space. On the other hand, as the beginning of the creation of an updated (new) nationally oriented education system. Education is a platform for the development of the country»s human and social capital. It has a polymorphic structure and its development is determined by a multiple set of external and internal factors. The main external factors are associated with the beginning of the formation of a new world economic order, and internal ones with the willingness of the country, the state and the people to «accept» it as reality and function in this reality. Conclusions . Analysis of such conditions shows that by leaving the Bologna system, Russia emphasizes two vectors of development of its education system: «distrust» of the Anglo-Saxon and Humboldtian models of the organization of educational space and the need to develop a new architecture of its own, national (different from global variants) type of such space. As main goal of such space is proclaimed the creation of nationally oriented technological, cognitive and educational sovereignties.
Potential of hikes in the educational process in the conditions of (post)pandemic and total digitalization

Introduction . The total use of digital technology leads in the long term to the opposition of educational and pedagogic processes. Their union is thought through the creation of institutional conditions to support learning in the form of hiking and travel. Purpose setting . The goal of the research is to identify the importance of local lore and tourism in the educational space and their capacity in the (post) pandemic. It is assumed that this is facilitated by the assertion of the priority of health in the mass consciousness and the release of people from mass labor. Methodology and methods of the study . On the basis of the content analysis of the literature the strengthening of the importance of tourism in the conditions of the pandemic and the dominance of information technology is deduced. Results . Learning in the form of hiking and travel is not separated from life and makes with it a whole. Personal qualities, attitudes, and values are manifested in the hike. The task of the teacher in such conditions is to «teach how to live». Outdoor education takes different forms in different countries; it is the most common type of situational learning among Scandinavian youth. Outdoor education during the pandemic takes on a special importance. It sterilizes all infections and strengthens immunity. In a pandemic, it is a good time to realize the healing value of beautiful landscapes. Fostering a love for native country and its beauty is also the way to make people healthier and improve their quality of life. Learning through camping and travel allows to overcome the division of geographic education into physical and economic geography. Tourism geography as a science of axiological (value) and educational and recreational resources of the geographical environment is considered. Conclusion . Tourism and local history serve as an effective means of filling students with exactly the knowledge that will be useful in life and should constitute the most important part of the educational process.
Forming of the personnel potential of higher education and science in conditions of external constraints

Introduction . In the structure of the total social potential, the basic role is assigned to the personnel potential, in the formation of which the social institutions of science and education take part. External restrictions caused by the spread of the novel coronavirus infection COVID-19 have led to the need for major changes in the existing system of training young research personnel. Purpose setting . To overcome the external constraints that have arisen, a number of recommendations have been promptly developed and a set of measures has been introduced to improve the quality of education and the attractiveness of the scientific and educational sector for young professionals, but at the same time, the technology for evaluating the effectiveness of the measures taken has not been fully developed. The purpose of the work is to identify the result of the measures taken and to identify the main contradictions that hinder the increase in the attractiveness of scientific and educational activities for graduates of higher educational institutions in the face of external restrictions. Methodology and methods of the study . The work uses sociocultural and structural-functional approaches, which made it possible to determine the potential vector and form of national universities development in the context of globalization under the influence of external constraints. The empirical base of the study was data obtained by using sociological methods in the course of the annual study of the employment trajectories of graduates of the Novosibirsk National Research State University. Results . It is shown that the measures and recommendations taken involve a number of actions not only at the federal level, but also at the level of universities, including tactics and strategy. An analysis of empirical data obtained in the framework of sociological studies of the labor preferences of graduates showed an increase in the interest of young professionals in the public sector of science and education during the lockdown period. With the removal of restrictions, the percentage of young researchers involved in science is reduced, while in the field of education the dynamics remain positive. Conclusion . As part of the lockdown, the measures taken were sufficiently effective to allow the domestic higher school to successfully localize and continue its activities in the national educational space.
SPEAKING ENGLISH POLICY: IMPACT ON THE ENGLISH LANGUAGE PROFICIENCY OF THE ESL

Learning English for different purposes, especially in the workplace has drastically changed. English increases the chances of getting a good job in any local or multinational company. It is also the language of communication, it is used in the media and the internet community, so learning English is important for socializing and entertainment as well as work. It is essential for individuals and future professionals to have profound knowledge and understanding of the English language that is used in any respective field. The study was conducted to weigh up the English language proficiency skills of a selected private school in the City of Malolos, particularly the grade ten (10) students who implement the English Only Policy and another secondary government school in the same city of Bulacan who do not implement the English Only Policy. The impact and factors of the English-Only Policy on students were measured and identified. The researcher utilized an English proficiency test from Cambridge University as the research instrument, which was given to the 80 respondents. The study is comparative that utilized an independent t-test in analyzing the data gathered. The results showed that there is a significant difference in the English language proficiency skills of the students who come from schools that have an English-only policy and those students who come from a school that does not implement the said policy. The research recommends that the students be more engaged and motivated to increase their English language proficiency skills. The teachers should also motivate their students to not only use the English language but also encourage them to use their L1.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0059/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Joint Latency and Energy Optimization of Online Task Offloading Algorithms for Vehicular Edge Computing

With the advantages of low latency and easy deployment, vehicle edge computing can effectively solve the task offloading problem for vehicles as well as surrounding pedestrians. However, due to hardware device limitations, vehicle edge nodes often have limited computing power and need to draw on the free resources on other surrounding vehicles. And the mobility of the vehicle also poses a challenge for task offloading. A common solution to this type of problem is Multi-Arm Bandit (MAB) theory, but existing algorithms based on MAB are not applicable to the dynamic vehicle environment. In this paper, we design an online learning-based vehicle task offloading algorithm (OLUCB), which performs task offloading in a distributed manner without frequent acquisition of vehicle state information, and adds three online capabilities of input-awareness, occurrence-awareness and speed-awareness to adapt to dynamic environments. The algorithm learns the task offloading performance of the surrounding vehicles and converges quickly to the optimal helper vehicle (HEV) with guaranteed performance. Extensive experiments in realistic highway scenario have been conducted, and results illustrate that the OLUCB achieves low latency and low energy performance. Compared to existing task offloading algorithms based on adaptive learning, the average latency, average energy consumption, average cost and average bit cost are reduced by 3%, 51%, 43% and 42% respectively.
Adventist Education: A Life-transforming Experience for Students, Parents, Educators, and Church Members in the Euro-Asia Division

TOGETHERzil and through the conference, the energy and excitement was palpable! 6 From Concept to Action in Democratic Republic of Congo (DRC)To illustrate the power that an idea can have, consider this example: Rudatinya Mwangachuchu, North East Congo Union Mission president, was one of the attendees.Upon his return to Goma, DRC, he lost no time in implementing lessons learned.The union mission purchased a house and barriers to enrollment, and outcomes of enrollment in Adventist schools such as improved retention in church, higher rates of marriage to an Adventist, and mature faith development.A statement on Adventist education was voted at Annual Council 2016 (see Sidebar), and this was accompanied by a cascade of initiatives across the world field, some of which are described in this article. K-12 EducationChurches in the Shape of Schools Andrew Mutero, director of education for the East-Central Africa Division (ECD), and Edgard Luz, then director of education for the South American Division (SAD), collaborated to help ECD develop capacity, expand education, and build schools.From February 1 to 13, 2017, a group of African church and education leaders visited primary, secondary, and tertiary schools in São Paolo and Brasilia, Brazil, to study school design, learn about best practices in marketing and managing schools, and develop action plans for advancing the ministry of education in their institution or field.They also observed the role of the Brazil Publishing House in supporting education.The group included union leaders, especially ECD Board of Education members, university council chairs, chancellors, institutional managers/treasurers, publishing leaders, and selected other union and conference administrators.The SAD provided an inspiring model for Africa.President Erton Ko hler explained, "We don't have schools.Rather, we have churches that we build in the shape of schools!" Schools are a powerful engine for church growth and discipleship in this division, where the church is young and energetic.These concepts found a ready reception among the African leaders.Immediately upon returning from Brazil, the group participated in one of the four regional LEAD conferences, this one held in Rwanda, for church and educational leaders from ECD, the Southern Africa-Indian Ocean Division, and the West-Central Africa Division.Informed by what they learned in Bra-5
Overall-Distinctive GCN for Social Relation Recognition on Videos

Recognizing social relationships between multiple characters from videos can enable intelligent systems to serve human society better. Previous studies mainly focus on the still image to classify the relationships while ignoring the important data source of the video. With the prosperity of multimedia, the methods of video-based social relationship recognition gradually emerge. However, those methods either only focus on the logical reasoning between multiple characters or only on the direct interaction in each character pair. To that end, inspired by the rules of interpersonal social communication, we propose Overall-Distinctive GCN (OD-GCN) to recognize the relationships of multiple characters in the videos. Specifically, we first construct an overall-level character heterogeneous graph with two types of edges and rich node representation features to capture the implicit relationship of all characters. Then, we design an attention module to find mentioned nodes for each character pair from feature sequences fused with temporal information. Further, we build distinctive-level graphs to focus on the interaction between two characters. Finally, we integrate multimodal global features to classify relationships. We conduct the experiments on the MovieGraphs dataset and validate the effectiveness of our proposed model.
MTGP: Combining Metamorphic Testing and Genetic Programming

Genetic programming is an evolutionary approach known for its performance in program synthesis. However, it is not yet mature enough for a practical use in real-world software development, since usually many training cases are required to generate programs that generalize to unseen test cases. As in practice, the training cases have to be expensively hand-labeled by the user, we need an approach to check the program behavior with a lower number of training cases. Metamorphic testing needs no labeled input/output examples. Instead, the program is executed multiple times, first on a given (randomly generated) input, followed by related inputs to check whether certain user-defined relations between the observed outputs hold. In this work, we suggest MTGP, which combines metamorphic testing and genetic programming and study its performance and the generalizability of the generated programs. Further, we analyze how the generalizability depends on the number of given labeled training cases. We find that using metamorphic testing combined with labeled training cases leads to a higher generalization rate than the use of labeled training cases alone in almost all studied configurations. Consequently, we recommend researchers to use metamorphic testing in their systems if the labeling of the training data is expensive.
Research on Classroom Teaching Evaluation Model Based on Big Data

In the digital society, education is faced with the problem of big data processing and application.The premise of using the comprehensive evaluation model to evaluate classroom teaching is how to deal with the huge original evaluation data.This paper uses the data cleaning technology of big data to preprocess the original evaluation data, so that the data is more standardized and effective, and important data can be extracted for calculation.Then the multi-level fuzzy comprehensive evaluation model is used to calculate the processed data, and the first level fuzzy comprehensive evaluation and the second level fuzzy comprehensive evaluation data are obtained.According to the maximum membership principle, the classroom evaluation results are obtained.
Research on Fusion of Electric Power Industry and External Data Based on Knowledge Map

Aiming at the problem of multi-source heterogeneous data fusion and utilization between the power industry and the outside, an application idea of internal and external data fusion based on knowledge map is proposed.Through the analysis of the existing mainstream technical routes of text information extraction and knowledge map construction, this paper puts forward an optimized text information extraction scheme and knowledge map construction scheme, and realizes the support of multi-source heterogeneous data fusion modeling.The research results have been applied in a provincial electric power company for risk identification in the field of engineering project process management, and the feasibility of the proposed technical scheme has been verified.
MM-Locate-News: Multimodal Focus Location Estimation in News

The consumption of news has changed significantly as the Web has become the most influential medium for information. To analyze and contextualize the large amount of news published every day, the geographic focus of an article is an important aspect in order to enable content-based news retrieval. There are methods and datasets for geolocation estimation from text or photos, but they are typically considered as separate tasks. However, the photo might lack geographical cues and text can include multiple locations, making it challenging to recognize the focus location using a single modality. In this paper, a novel dataset called Multimodal Focus Location of News (MM-Locate-News) is introduced. We evaluate state-of-the-art methods on the new benchmark dataset and suggest novel models to predict the focus location of news using both textual and image content. The experimental results show that the multimodal model outperforms unimodal models.
Decentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration

We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clients and where different clients have different local learning tasks. We study both covariate and label shift, and our contribution is an algorithm which for each client finds beneficial collaborations based on a similarity estimate for the local task. Our method does not rely on hyperparameters which are hard to estimate, such as the number of client clusters, but rather continuously adapts to the network topology using soft cluster assignment based on a novel adaptive gossip algorithm. We test the proposed method in various settings where data is not independent and identically distributed among the clients. The experimental evaluation shows that the proposed method performs better than previous state-of-the-art algorithms for this problem setting, and handles situations well where previous methods fail.
Secure File Outsourcing Method Based on Consortium Blockchain

With the development of the big data era, the amount of data has entered an explosive growth phase. Limited by the constraints of cost, efficiency, and security of self-built storage systems, enterprises are forced to outsource files to cloud storage systems. However, the lack of file security and auditability in cloud storage systems continues to threaten the security of outsourced files. This paper designs and implements BFSOut, which is a secure file outsourcing method based on consortium blockchain. It uses Hyperledger Fabric and Interplanetary File System (IPFS) as the underlying storage engine, which solves the problem of cloud storage security issues. In BFSOut, in order to ensure the security of outsource files, the client-side offline block encryption is used. Furthermore, a dynamic hybrid encryption scheme is adopted, making the overall encryption effect more Efficient. Experimental performance analysis show that the system has good performance.
Design and Analysis of Two Efficient Socialist Millionaires’ Protocols for Privacy Protection

Yao's Millionaires’ problem has led to the emergence of secure multi-party computation. As an important tool for privacy protection in cryptography, secure multi-party computation has attracted more and more scholars to study it. The socialist millionaires’ problem is the basic module of the secure multiparty computing protocol. Designing secure and efficient solutions for the socialist millionaires’ problem can be effectively applied to the secret ballot, electronic auction, and so on. Based on the vector encoding method, the Paillier encryption scheme, and the Goldwasser-Micali encryption scheme, two efficient socialist millionaires’ protocols are proposed and the protocols are analyzed. The correctness analysis, security proof, performance analysis, and experimental simulation show that the efficiency of the two protocols is superior to the existing schemes.
A Reliable Novel Approach of Bio-Image Processing—Age and Gender Prediction

Image processing in its field has many applications. With the advancement in the deep learning, many researchers experimented on various traits recognition of face. One of the best applications is age prediction. Using various location points on the face, the age is predicted from the face. Similarly gender also. Age and gender prediction is which allows us to predict age and gender from a texture image or real time video. Important application of age and gender prediction is in Biometrics which is used for security purposes. This paper presents the results on gender prediction and age estimation system based on convolutions neutral networks by extracting features from given input image and reorganization by taking large data set and dividing it into training data (80%) and testing data (20%). The proposed system can get accurate results by taking large sets of training data. The proposed method used ResNet architecture using facial points identification, to classify the age group and gender of the input subject. The experimentation achieved the accuracy of 84% in predicting the age and 71% predicting the gender.
Efficient Motion Detection and Compensation Using FPGA

Moving target detection plays a vital role in computer vision applications, which requires rigorous algorithms and high computation; also, realization of these algorithm in real time is difficult. Hence, in this paper, FPGA implementation of moving object detection by correcting the unwanted motion is proposed. The sub-components of the architecture are optimized to get the optimization of the entire module. Also, the performance of the proposed technique is validated by calculating the different performance metrics, and the method is compared with some of the method from literature. The experimental results illustrate the approach is excellent in detecting the moving objects even when the camera is moving and less hardware is utilized to detect the moving targets efficiently. The designed architecture is implemented on Xilinx 14.5 Zynq Z7-10 series FPGA development board.