Reviewerly/exHarmony huggingface dataset:
Design Thinking in Designing Career Guidance Services for High School Students

This article examines the application of design thinking methodology in the design process of career guidance services for high school students, thereby revealing the specifics of this method. The author considers and comments on the main stages of design thinking and identifies design thinking tools for the development of career guidance services. The methods were selected for each stage of design thinking separately. Thus, a unique approach of service development was formed. Approach suggested by the authors consists of three stages with structured set of actions: the preparatory stage, the development stage, which includes the choice of software architecture and the choice of tools for development and the final stage of testing, bug fixes. Based on the elaborated approach a service operation model was also built with key success factors of the service implementation identified. Singavio platform and BPMN notation were used to create service operartion model.
Conceptual Model for Assessing the Formation of Universal Competencies in the Implementation of End-To-End Project Activities

Modern educational standards are focused on the formation of universal competencies among students, and the project format is almost the only way to form the necessary flexible competencies. The ability to conduct project activities becomes one of the main meta-competencies. The most important element of the new edition of the educational policy of Peter the Great St. Petersburg Polytechnic University, published in 2021, is the concept of end-to-end project activities and the ecosystem of project activities. The implemented approach imposes appropriate requirements on the quality of graduate training, which, in turn, requires an appropriate system for measuring and evaluating the level of formation of universal competencies. The creation of such a system is an urgent problem now. The paper proposes a conceptual model for assessing the formation of universal competencies in the implementation of end-to-end project activities. Given the complexity of the internal structure of universal competence and the fact that the process of their formation is nonlinear, it is proposed to use “learning curves” for evaluation. For this purpose, the logistic form of the curve is considered. The concept of “project points” is introduced. A formal procedure for calculating project points is proposed, taking into account various characteristics of end-to-end project activities. The proposed approach to the assessment of universal competencies was tested at SPBPU for undergraduate courses. It is planned to test it as part of end-to-end project activities for undergraduate and graduate students in the framework of the Priority 2030 program.
Breaking the Silence: Convolutional Neural Networks for Sign Language Recognition in the Deaf Community

The deaf community faces communication barriers that hinder their interaction with the hearing world. This work aims to bridge the gap by enabling accurate recognition of Arabic sign language gestures. The proposed Convolutional Neural Networks (CNNs) architecture is designed to effectively capture the spatial features inherent in sign language gestures, thereby enhancing recognition accuracy. A distinctive aspect of our work involves the integration of a CNN architecture with a residual design, which effectively captures intricate spatial features inherent in sign language gestures, thereby enhancing recognition precision. The study leverages the ArSL2018 dataset, a comprehensive collection of grayscale sign language images with diverse lighting conditions and backgrounds. Our custom-built CNN model is trained on this dataset, utilizing a specialized learning rate scheduler for improved convergence. The experimental results showcase promising performance, demonstrating the potential of CNNs in sign language recognition. Furthermore, we present visualizations of the model's predictions using t-SNE, revealing the clustering patterns of different sign language gestures.
Exploration of academic research trends of early childhood education in Taiwan through CATAR

Young children are fundamental elements of societal progress. Consequently, cultivating adeptness in early childhood education and childcare talents assumes paramount significance, with educators within tertiary colleges and universities taking a pivotal role therein. Furthermore, research literacy is what educators develop through applying theoretical knowledge and is deemed an indispensable competency in effective instruction. This proficiency is manifested in formulating superlative curricula and augmenting scholastic prospects for students. Hence, the primary objective of this study is to analyze the global academic progression within the domain of early childhood education across Taiwanese tertiary institutions from 2012 to 2022. Employing the content analysis toolkit for academic research technology, the investigation discloses three focal points of research interest: children’s health, preschool curriculum design, and the professional competence of preschool educators. The inquiry discerns that personnel within Taiwan’s higher education sphere have evinced remarkable research scholarly proficiency in early childhood education. This not only bolsters Taiwan’s international scholarly standing but also emboldens nascent researchers to undertake more profound explorations within this academic arena, thereby amplifying Taiwan’s global academic influence in early childhood education.
A template-based approach for question answering over knowledge bases

Abstract In this paper, we address the problem of answering complex questions formulated by users in natural language. Since traditional information retrieval systems are not suitable for complex questions, these questions are usually run over knowledge bases, such as Wikidata or DBpedia. We propose a semi-automatic approach for transforming a natural language question into a SPARQL query that can be easily processed over a knowledge base. The approach applies classification techniques to associate a natural language question with a proper query template from a set of predefined templates. The nature of our approach is semi-automatic as the query templates are manually written by human assessors, who are the experts of the knowledge bases, whereas the classification and query processing steps are completely automatic. Our experiments on the large-scale CSQA dataset for question-answering corroborate the effectiveness of our approach.
Artificial Intelligence : in Creation and Destruction of Human Creativity pertaining to the Schumpeter’s Gale

Technological innovation stands as a crucial element in the advancement of humanity. Artificial Intelligence (AI) is a cutting-edge innovation that is highly sought after in the present times. This powerful and novel technology profoundly influences numerous aspects of our lives. The extensive and swift integration of AI tools in various domains has prompted conversations regarding the significance of AI and its potential influence on creativity. The main objective of this research paper is to conduct a thorough investigation into the impact of AI on different facets of human creativity. It will explore the potential applications of AI as a supportive instrument to augment and facilitate human creative endeavours. Additionally, the paper will delve into the potential risks and drawbacks associated with AI’s involvement in artistic pursuits, highlighting its potentially detrimental effects and proposing possible solutions to safeguard our creative expression. This research integrates Joseph Schumpeter’s ‘Creative Destruction’ theory (Schumpeter’s Gale), to analyse why AI is viewed as a Creative Destruction in Human Creativity.
Attention-guided Adversarial Attack for Video Object Segmentation

Video Object Segmentation (VOS) methods have made many breakthroughs with the help of the continuous development and advancement of deep learning. However, the deep learning model is vulnerable to malicious adversarial attacks, which mislead the model to make wrong decisions by adding adversarial perturbation that humans cannot perceive to the input image. Threats to deep learning models remind us that video object segmentation methods are also vulnerable to attacks, thereby threatening their security. Therefore, we study adversarial attacks on the VOS task to better identify the vulnerabilities of the VOS method, which in turn provides an opportunity to improve its robustness. In this paper, we propose an attention-guided adversarial attack method, which uses spatial attention blocks to capture features with global dependencies to construct correlations between consecutive video frames, and performs multipath aggregation to effectively integrate spatial-temporal perturbation, thereby guiding the deconvolution network to generate adversarial examples with strong attack capability. Specifically, the class loss function is designed to enable the deconvolution network to better activate noise in other regions and suppress the activation related to the object class based on the enhanced feature map of the object class. At the same time, attentional feature loss is designed to enhance the transferability against attack. The experimental results on the DAVIS dataset show that the proposed attention-guided adversarial attack method can significantly reduce the segmentation accuracy of OSVOS, and the J &amp; F mean on DAVIS 2016 can reach 73.6% drop rate. The generated adversarial examples are also highly transferable to other video object segmentation models.
Determining primary school teachers’ readiness to develop reflexive skills in grade schoolers

The research aims to substantiate the problem of primary school teachers’ readiness to develop reflexive skills in grade schoolers. The paper considers the problem of the development of reflexive skills in grade schoolers from different perspectives; identifies primary school teachers’ readiness to develop reflexive skills in grade schoolers; demonstrates and analyses the results of a survey of primary school teachers. The scientific novelty of the study consists in clarifying the notion of “reflexive skills of a grade schooler” and elaborating a questionnaire to determine teachers’ readiness to develop these skills at the required level in the classroom. As a result, the results of the survey have been analysed and summarised, the main area of further methodological work with teachers on the development of reflexive skills in grade schoolers in the classroom has been determined.
TRIZ technology as a tool for creative thinking development in the system of foreign language training for foreign students of non-linguistic majors

The paper aims to substantiate that the use of TRIZ technology (the theory of inventive problem solving) has a high methodological potential, since it contributes to a deeper analysis of the situation under study, activates critical, logical and creative thinking and also promotes the formation of students’ communicative skills through teamwork. The paper describes the prerequisites for the development of TRIZ pedagogy, within the framework of which the authors elaborated an algorithm for stimulating foreign students’ creative thinking in foreign language classes. The algorithm was tested in the 2021/2022 academic year at Peter the Great St. Petersburg Polytechnic University and proved its effectiveness by demonstrating an increase in the indicators of creativity criteria (productivity, originality, curiosity, accuracy) and positive dynamics of interaction in the study group. The scientific novelty of the research lies in the elaboration of the algorithm for teaching foreign students English based on TRIZ technology. As a result, it was proved that the described and tested algorithm, consisting of the following steps: (1) warming up, (2) analysis, (3) reflection, (4) generation, (5) selection, (6) summary, can be used for teaching English in order to develop creative thinking.
Reinforcement learning for the traveling salesman problem: Performance comparison of three algorithms

Abstract Travelling salesman problem (TSP) is one of the most famous problems in graph theory, as well as one of the typical nondeterministic polynomial time (NP)‐hard problems in combinatorial optimization. Reinforcement learning (RL) has been widely regarded as an effective tool for solving combinatorial optimization problems. This paper attempts to solve the TSP problem using different reinforcement learning algorithms and evaluated the performance of three RL algorithms (Q‐Learning, SARSA, and Double Q‐Learning) under different reward functions, ε‐greedy decay strategies, and running times. A comprehensive analysis and comparison of the three algorithms mentioned above were conducted in the experiment. First, the experimental results indicate that the Double Q‐Learning algorithm is the best algorithm. Among the eight TSP instances, the Double Q‐Learning algorithm outperforms the other two algorithms in five instances. Additionally, it has shorter runtimes compared to the SARSA algorithm and similar runtimes to the Q‐Learning algorithm across all instances. Second, upon analysing the results, it was found that using the reward strategy contributes to obtaining the best results for all algorithms. Among the 24 combinations of 3 algorithms and 8 instances, 17 combinations achieved the best results when the reward strategy was set to .
Influence of Geo-Technologies Integration on the Learning of Geography among Public Secondary Schools

Technology integration has become critical globally in pursuit of transforming education. In Siaya County, a persistent dismal mean of scores in Geography subjects among public secondary schools from 2016-2022 at Kenya Certificate of Secondary Examination has been blamed on the learning approaches. The data from the seven sub-counties indicated that very little learning occurred. Further, no known study has addressed rote learning in Geography using an explanatory research design about Siaya County. The study sought the influence of integrating geo-technologies on learning in Geography among public secondary schools in Siaya County. This study focused on Geo-technology integration influencing learning among public secondary schools in Siaya County, Kenya. The objective that guided this study was to explore the influence of a geo-technology integrated classroom environment on learning Geography among public secondary schools in Siaya County, Kenya. The study sought to answer the question; during 2016-2022, how did the integration of geo-technology in the classroom environment influence the learning of Geography in public Secondary Schools in Siaya County? The study adopted interviews, questionnaires, and observational checklists to collect data. The study employed social cognitive constructivism theory to envision effective learning. The study sampled 102 respondents from 262 public secondary schools based on Krejcie and Morgan’s table. The choice of Siaya County was based on the fact that it is the only county in the larger Nyanza where technology integration was piloted by New Partnership for Africa’s Development (NEPAD) for quality learning. This study employed a mixed method and explanatory design to carry out the study. The study adopted interviews, questionnaires, and observational checklists to collect primary and secondary data.
On the performance and scalability of consensus mechanisms in privacy-enabled decentralized renewable energy marketplace

Abstract Renewable energy sources were introduced as an alternative to fossil fuel sources to make electricity generation cleaner. However, today’s renewable energy markets face a number of limitations, such as inflexible pricing models and inaccurate consumption information. These limitations can be addressed with a decentralized marketplace architecture. Such architecture requires a mechanism to guarantee that all marketplace operations are executed according to predefined rules and regulations. One of the ways to establish such a mechanism is blockchain technology. This work defines a decentralized blockchain-based peer-to-peer (P2P) energy marketplace which addresses actors’ privacy and the performance of consensus mechanisms. The defined marketplace utilizes private permissioned Ethereum-based blockchain client Hyperledger Besu (HB) and its smart contracts to automate the P2P trade settlement process. Also, to make the marketplace compliant with energy trade regulations, it includes the regulator actor, which manages the issue and consumption of guarantees of origin and certifies the renewable energy sources used to generate traded electricity. Finally, the proposed marketplace incorporates privacy-preserving features, allowing it to generate private transactions and store them within a designated group of actors. Performance evaluation results of HB-based marketplace with three main consensus mechanisms for private networks, i.e., Clique, IBFT 2.0, and QBFT, demonstrate a lower throughput than another popular private permissioned blockchain platform Hyperledger Fabric (HF). However, the lower throughput is a side effect of the Byzantine Fault Tolerant characteristics of HB’s consensus mechanisms, i.e., IBFT 2.0 and QBFT, which provide increased security compared to HF’s Crash Fault Tolerant consensus RAFT.
An investigation of solar energy potential towards improving agriculture using angstrom and newly developed analytical models: In case of The Gambia

Cleaner and affordable energy demand in The Gambia has gained a lot of prominence in recent years, with many people shifting towards solar PV installation. However, the interest in utilizing solar energy has continued to witness a remarkable setback in developing countries due to economic factors and the limitations of in situ measurements. The latter has steered researchers to explore empirical models to predict global solar radiation with the aid of available meteorological data. This study estimates monthly global solar radiation (HG) using the modified Angstrom models and a newly developed model at two locations in The Gambia using secondary data collected from the Yundum and Basse meteorological stations for twenty years (2000-2019) to evaluate the prospects for the siting and implementing of solar projects. The results ranged from 9.0-27.0 MJm−2day−1 or 2.5-7.5 kWh/m2/day for Yundum and 8.0-28.0 MJm−2day−1 or 2.2-7.8 kWh/m2/day for Basse. The models’ results were also compared with the solar resource maps and GIS data for The Gambia locations and tested with the statistical tools of Root mean square error, Correlation coefficient, Normalized mean square error, and Mean percentage error to validate their performances and found to show a good agreement on monthly basis. However, the success of these models drastically declined when estimated on daily or hourly basis. The potential of utilizing the outcome in the agriculture sector of the economy was also x-rayed and justified. Even the lowest value of 8.0 MJm−2day−1which occurred in December has the potential of generating solar energy. This signifies that solar energy can be explored to make up for the energy shortfall in The Gambia's agriculture and other sectors of the economy. The modified Angstrom equations for Yundum and Basse and our new model used in estimating the HG and the regression parameters can be applied to estimate the HG for the subsequent years at the same locations. We recommend that similar studies be conducted at other locations using our newly developed model for comparison and generality of the result.
Pedagogical dynasties of the Amur region: The Pankratz dynasty

The aim of the study is to expand on the historical and pedagogical knowledge about the pedagogical dynasty of the Pankratz family, whose representatives carry out their professional activities in the Amur region. The paper provides information about the professional activities undertaken by the representatives of the pedagogical dynasty of the Pankratz family, specifies its features and type. The relevance of the study is determined by the need of society to master the professional experience transmitted by pedagogical dynasties, as well as the desire of the state to increase the prestige of the teaching profession and the quality of teacher education. The scientific novelty of the study lies in enriching historical and pedagogical scientific knowledge with the information about the Pankratz dynasty, which will allow it to be included in the unified database of pedagogical dynasties of the Amur region. As a result, it has been proved that the pedagogical dynasty of the Pankratz family possesses the features of pedagogical dynasties and is characterised as mature, multi-generational, average in composition, mixed by the type of intra-family ties, specialisation and level of education. From generation to generation, the representatives of the dynasty share and transmit their accumulated professional experience, ideals, moral values and traditions to the cultural space, which makes this dynasty socially significant.
Research on rural employment and entrepreneurship of undergraduates in the context of a rural revitalization strategy

Abstract Exploring rural employment and entrepreneurship of undergraduate graduates in the context of rural revitalization strategy is conducive to providing human resources for rural revitalization. This paper starts from the Gaussian mixture model, introduces the specific principle of the Gaussian mixture model, then introduces the EM algorithm under the data mining algorithm, and points out that the EM algorithm is often used as an iterative optimization method for great likelihood estimation, and explains the principle and process steps of the algorithm in detail. The EM algorithm is used to solve the logarithmic great likelihood estimate of the Q function with conditional probability density function, and then the EM algorithm is applied to the Gaussian mixture model to update the values of its parameters, and the performance of the EM-GMM algorithm is also evaluated. Finally, the EM-GMM algorithm analyzes the impact of employment unit preference and employment promotion policies on the undergraduate graduates of four universities in a certain area. From the performance evaluation, the algorithm’s accuracy in this paper is 94.54%, which is 21.29 and 5.02 percentage points higher than that of the K-means-EM algorithm and FCM-EM algorithm, respectively. Regarding employment unit preference, 45.82% and 22.09% of undergraduate graduates are more inclined to be employed in township government agencies, state-owned enterprises, and institutions. Promoting rural employment and entrepreneurship of undergraduate graduates requires strengthening employment guidance and employment concept correction to provide fresh blood for rural revitalization.