Reviewerly/exHarmony huggingface dataset:
The use of multimedia technologies in robotics lessons as the development of cognitive activity

In the environment of educational robotics, the introduction of new pedagogical technologies is relevant. Robotics is at the junction of promising fields of knowledge: mechanics, electronics, automation, assembly, programming, circuit engineering and technical design. The purpose of the article is to describe multimedia software in lessons on the basics of robotics and teaching methods using multimedia technologies at each stage of training. The research methods were analysis, synthesis, and conversation. The authors of the article have developed Digital educational resources for the study of robotics. These resources are an auxiliary tool for the diversity of the lesson. With the help of this software product, robotics training becomes more accessible and convenient. Multimedia allows you to combine verbal and visual-sensory information, which helps to increase the motivation of students, create certain learning parameters and develop cognitive activity. When organizing educational material, the teacher should focus on the activation of cognitive activity of students. In the classroom, not all students turn on active cognitive activity. Active teaching methods are used to purposefully influence this activity. The very name contains the essence of the active method - it is not simple memorization, but the active intellectual activity of students. The result of this study is a digital learning center with the help of which it is possible to conduct lessons using multimedia technologies. Keywords: educational robotics, multimedia technologies, cognitive activity, digital educational resources.
Android-Based Health Service Center Search Application in Tasikmalaya City Using Location Based Service (LBS) Method

Health Service is a place to organize health efforts. Not all health service locations are known to the public, both those from Tasikmalaya City and those from outside the city. This is due to a lack of information about health services in the City of Tasikmalaya. Progress and development of the times, as currently the use of technology is needed to be able to provide information quickly, where the use of technology can be accessed anytime and wherever the user is. The use of Information Technology such as using an Android-based Smartphone Application can be used to help citizens to find a location and find out the route to the desired health service. This study aims to build an Android-based Health Service Search application as a source of information to find out the location and routes to service locations. health services in Tasikmalaya City. The research method used is the waterfall process model. Implementation of health service applications using Javascript programming with Android Studio, Location Based Service, and maps sourced from the Google Maps API. The results of the research are in the form of Android-based Tasikmalaya City health service applications that help make it easier for Tasikmalaya City residents and outside the region to obtain information about Health Services and routes from the user's location to the desired Health Service location in Tasikmalaya City with the help of GPS.
READINESS OF VOCATIONAL TEACHERS FOR RESEARCH ACTIVITIES DEPENDING ON THE TYPE OF ORGANIZATION

The vocational teachers’ desire to follow innovative trends in the system of vocational education and the labour market makes the issue of the qualification level required for research activities as a key component to professional development relevant. The purpose of the article: find out if there are statistically significant differences in level of formed research competencies of the vocational teachers depending on the type of educational organization (vocational, service, humanities, etc.). Methods. The key method for collecting empirical data is the method of interviewing teachers based on self-assessment of their own research competencies on a 5-point scale applying the tools developed by the authors. Methods for data analysis using specialized software for calculating statistical indicators (SPSS 20): a method for calculating the parameters of descriptive statistics and a method for comparative analysis of independent samples using the Kruskal - Wallis criterion. Results. A model of research competencies (associated with the stages of scientific research and various types of this activity) necessary for teachers in their professional and pedagogical activity is presented. The paper provides the results of the study that revealed specific features in the implementation of research activities and differences in the deficit of research competencies among vocational teachers (n = 573) depending on the type of educational organization (vocational, service, humanities, etc.): the lowest level of formed competencies was determined among teachers of polytechnic schools and interdisciplinary colleges, the highest – among teachers and masters of vocational training of humanities and pedagogical colleges. Successful practices in filling the identified deficits in research competencies are specified.
Data monitoring in student psychological automatic evaluation system by using dynamic acquisition algorithm

The purpose of designing embedded Internet of things and studying the ad hoc technology of gateway is to meet the monitoring requirements brought by the continuous expansion of data center. Among them, in the aspect of dynamic data monitoring, this paper designs a dynamic acquisition algorithm, that is, by analyzing the characteristics of data center resources and the number of information acquisition methods, so as to adjust the data acquisition cycle and to reduce the monitoring workload and improve the monitoring flexibility. At the same time, this paper also studies the automatic psychological evaluation system of students. It is found that with the intensification and efficient operation of social competition, some special groups of college students are facing increasing pressure, and events caused by psychological problems emerge one after another. Even though there are many psychological evaluation systems on the network, a large number of psychological knowledge and mental health management on the Internet cannot effectively manage and improve the psychological status of college students. Therefore, the university needs to build a psychological evaluation system for college students in time and combine the automatic psychological evaluation system for college students with embedded Internet of things and cloud service technology to improve the accuracy of psychological evaluation. The application of the system, college students can independently evaluate their own mental state, if there are psychological problems can be timely counseling, not only can improve the level of mental health education in colleges and universities, but also can promote the development of physical and mental health.
Prompt fine-tuning-based efficient federated foreign object detection of electric transmission line

Foreign objects such as bird’s nests, honeycombs, kites, and plastics threaten the safety of electric transmission lines. Training a foreign object detection model with high generalization and accuracy needs to collect numerous data, which is usually unavailable because of data privacy and security. Federated learning is a promising solution but the total calculation amount is huge, and large number of gradient data will be transmitted in federated learning model updating. And because the data amount, training efficiency, and model accuracy are different at edge clients, it is not appropriate to aggregate clients’ edge models equally. To address the above mentioned problems, a prompt fine-tuning based efficient federated foreign object detection method of electric transmission line, which includes prompt federated fine-tuning mechanism and self-adaptive dynamic weighting aggregation strategy, is proposed in this paper. Prompt federated finetuning freezes the model backbone in edge client model training, and only the unfrozen parts of the model are transmitted and updated in the model aggregation phase. Self-adaptive dynamic weighting aggregation mechanism takes local data amount, edge model’s accuracy, and aggregation rounds into account when aggregating local models. Comprehensive experimental results demonstrate that the proposed method has not only better model accuracy but also faster training speed compared with other federated learning baselines on transmission line foreign object detection.
Application of K-means clustering algorithm for academic performance analysis in students’ college Japanese course

Over the past few years, many universities are starting to set up the college Japanese course for undergraduate students. But many new situations and problems arise. So far, there has been relatively little research on these issues, not to mention research using an empirical approach. To address these issues and gain practical insights, this paper first collects the learning dataset of all first-year students taking a college Japanese course at a university. The program has a stable learning population, and a good research foundation that lasts for one year. Second, a clustering algorithm called KMeans was used to evaluate the student learning analysis. Specifically, we use the elbow technique to identify the Kvalues that work best. The empirical analysis showed that clusters performed best when the number of clusters was 2 which means students should be divided into two groups：the students evaluated as Good (49.43%), and the students evaluated as Fair (50.57%). In addition, we analyzed the learning performance of both groups and made practical suggestions for teaching. The findings of our study can provide a practical reference for higher education institutions and teaching staff.
BP-based supervised learning algorithm for multilayer photonic spiking neural network and hardware implementation

We introduce a supervised learning algorithm for photonic spiking neural network (SNN) based on back propagation. For the supervised learning algorithm, the information is encoded into spike trains with different strength, and the SNN is trained according to different patterns composed of different spike numbers of the output neurons. Furthermore, the classification task is performed numerically and experimentally based on the supervised learning algorithm in the SNN. The SNN is composed of photonic spiking neuron based on vertical-cavity surface-emitting laser which is functionally similar to leaky-integrate and fire neuron. The results prove the demonstration of the algorithm implementation on hardware. To seek ultra-low power consumption and ultra-low delay, it is great significance to design and implement a hardware-friendly learning algorithm of photonic neural networks and realize hardware-algorithm collaborative computing.
App interaction design in the pop music singing teaching system based on differential evolution algorithm

The dynamic spectrum management of cognitive technology can greatly increase the utilization of network spectrum, but while bringing convenience to our daily lives, this new technology will also greatly increase the consumption of nodes. At present, the short video industry is growing rapidly with the development of smart phones. Among them, the emergence of online popular music singing teaching platforms (mainly software for shooting short videos with music as the background) has brought significant changes to people’s lifestyles. We can learn from the three-stage theory of flow experience to design and explore the method of plan implementation, and propose a targeted interaction design strategy for each flow characteristic, so that the overall design strategy becomes more practical and practical. In the design of this paper, the principle of cognitive wireless sensor network is applied in the technical aspect and the principle of interactive design is applied in the product design. When a user reaches a state of flow while using the online popular music singing teaching platform, it can be evaluated that the user experience of the app is the best. The research on the interaction design of the online pop music singing teaching platform under the flow experience is intended to propose an interactive design strategy that can guide the online pop music singing teaching platform to better create a flow experience for users.
A Perception Study on the Transaction Value and Continuity of NFT in Digital Trade Environment

This study sought to understand users' perceptions of transaction value and its continuity using NFT on the premise of a blockchain-based digital trade environment at the same time as the 4th industrial revolution spreads. In reality, since there is a possibility of NFT and risk factors, the model was proposed by combining activation factors and concerns, and in detail, the theoretical model was proposed by setting NFT transaction safety and scarcity as activation factors and legal system uncertainty as concerns. As a result of conducting an empirical analysis along with data collection, significance was found in the path of NFT scarcity on transaction value, but no significant results were derived in the path of transaction safety on transaction value. In addition, the moderating effect of uncertainty in the legal system was found to be limited. The implications of the research results are that it is desirable to find integration in fields such as NFT-based bills of lading, certificates of origin, and trade contracts in a digital trade environment.
Research on Multi-Label Text Classification Based on Multi-Channel CNN and BiLSTM

Natural language processing research is moving in a significant improvement toward multi-label text classification. Multi-label text classification is presently used intensively in practical disciplines. In the text classification task, due to the professional and complex diversity of the text, it is difficult to fully represent the semantic of the text only by relying on the existing word vector representation method, which leads to the low accuracy of the classification task. In order to even get dynamic semantic information of text, this dissertation proposes a hybrid neural network model that incorporates use of Bert's pre-trained language model. Bert model has achieved remarkable results in text classification task. In order to analyze the text content, multi-channel CNN and BiLSTM are proposed for feature fusion. Simple CNN (Convolutional Neural Network) and RNN (Recurrent Neural Networks) cause problems such as loss of key feature information and poor classification performance when processing classification tasks. Multi-channel CNN uses different convolution kernels to obtain local semantic features of text, and BiLSTM model uses gating mechanism to obtain global information of text context. The ultimate vector representation of the text proposed in this study is the result of superimposing the two components mentioned above, and Sigmoid is exploited for classification. The suggested multi-label text classification method provides a stronger classification effect than existing models, thus according experimental results. It can be found that our model has achieved the maximum value in three indexes, with Precision <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">micro</inf> , Recall <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">micro</inf> and F1 <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">micro</inf> reaching 0.9455, 0.9181 and 0.9315.
Software Supply Chain Security of Power Industry Based on BAS Technology

The rapid improvement of computer and network technology not only promotes the improvement of productivity and facilitates people's life, but also brings new threats to production and life. Cyberspace security has attracted more and more attention. Different from traditional cyberspace security, APT attacks on key networks or infrastructure, with the main goal of stealing intellectual property, confidential information or sabotage, seriously threatening the interests and security of governments, enterprises and scientific research institutions. Timely detection and blocking is particularly important. The purpose of this paper is to study the security of software supply chain in power industry based on BAS technology. The experimental data shows that Type 1 projects account for the least amount and Type 2 projects account for the highest proportion. Type 1 projects have high unit price contracts and high profits, but the number is small and the time for signing orders is long.
Feasibility Analysis of E-Government Application Based on Blockchain Technology

The application of blockchain technology is no longer limited to cryptocurrencies. The decentralization, non-tampering, asymmetric encryption, traceability and other characteristics of blockchain technology just meet the needs of digital government affairs for data circulation security and credibility, and build a multi-party trust network through the consensus mechanism to further realize The deep integration of blockchain and government affairs optimizes government business processes, making government affairs open to the sun, transparent and credible. This paper analyzes the pilot project of blockchain technology cooperation between Chengdu M Information Technology Co., Ltd. and the Kyrgyz Republic in e-government, and discusses its feasibility in the application of Kyrgyzstan's e-government.
The face of IoT

Connectivity is shaping the way technology is evolving, leading to the increasing adoption of the so-called Internet of Things (IoT). In parallel, there is a rising trend of smart surfaces that, powered with IoT devices, become inherently interactive and responsive. While there is a plethora of opportunities to explore, this type of technology is already being employed across different industries, like healthcare, construction, transportation, and electronics. A smart surface can have many definitions, but for Ynvisible, it means an interactive and seamless human-machine interface based on their printed e-paper display.
CRYPTOVEST USING ANGULAR

Cryptography protects cryptocurrency, a digital or virtual version of money.Blockchain technology is used by the decentralized cryptocurrencies to protect transactions and regulate the creation of new units.A popular JavaScript framework for creating dynamic web apps is called Angular.In order to develop web apps that let customers buy, sell, and manage their bitcoin assets, this paper investigates the introduction of Angular with cryptocurrencies.We go over the advantages of deploying Angular for creating bitcoin applications as well as potential difficulties for developers.
A STUDY OF EDUCATIONAL THINKING OF GIJUBHAI BADHEKA AND ITS RELEVANCE IN THE MODERN ERA

In this paper, the researcher has discussed Gijubhai's life introduction, personality and the important ideas he gave in the field of education. It deals with various important educational methods offered by them. As Gijubhai has done all the important work to bring change in the field of education and what was his opinion is discussed. At the same time it is said that he created some of his own institutes and carried out child oriented work in that institute. Besides, the educational policies prepared by him are also used in the present times. This paper also discusses what he believed in the practice. It is important to note that parents' responsibility towards their children's education, study structure, teaching methods, mother tongue education, school environment, discipline, value education, examination system, social impact, etc. are discussed in this chapter. came. From which it is concluded that Gijubhai Badheka has always studied keeping in view the interest of the children and his idea of imparting education through various activities has proved to be very appropriate which can prove to be very useful for the children even in the present times.
Analisis Performa Algoritma NBC, DT, SVM dalam Klasifikasi Data Ulasan Pengunjung Candi Borobudur Berbasis CRISP-DM

The approach of visitor sentiment analysis to Borobudur Temple tourist destinations in Indonesia can be classified using various algorithms to get optimal results. Good algorithm performance can be seen from the confusion matrix (accuracy, precision, recall) value, Area Under Curve (AUC) value, and Receiver Operating Characteristic (ROC). This study used the Naïve Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM) algorithms against 3850 text data obtained from the Tripadvisor website, especially reviews of Borobudur Temple visitors. The method refers to the Cross-Industry Standard Process for Data Mining (CRISP-DM) for optimizing tourist destination products and services by paying attention to six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results of this study show that the results of NBC's algorithm performance evaluation can be seen to have a change in the confusion matrix value at the accuracy value from 98.73% to 95.6%, the precision value changed from 98.72% to 98.97%, the recall value also changed from 100% to 96.54%. In addition, the Area Under Curve (AUC) of NBC also changed from 0.500 (50%) to 0.693 (69.35%). In addition, the results of the DT algorithm performance evaluation showed a change in the confusion matrix value at the accuracy value from 97.55% to 94.40%, the precision value increased from 97.63% to 91.86%, the recall value also changed from 99.90% to 99.47%. The Area Under Curve (AUC) of DT value also changed from 0.591 (59.1%) to 0.932 (93.2%). The results of the SVM algorithm performance evaluation showed a change in the confusion matrix value at the accuracy value from 98.73% to 99.41%; the precision value changed from 98.72% to 100%, and the recall value also changed from 100% to 99.01%. The Area Under Curve (AUC) of the SVM value also changed from 0.961 (96.1%) to 1.00 (100%). In addition, the T-test results show that the SVM algorithm is more dominant compared to other algorithms, where the SVM algorithm T-test value is 0.994 compared to the DT algorithm T-test value of 0.944 and the NBC algorithm T-test value of 0.98. Based on the Receiver Operating Characteristic (ROC) value, it can be seen that the DT algorithm also shows good performance in addition to SVM. It indicates that in analyzing the sentiment of visitors to Borobudur Temple, the best-recommended algorithm is the Support Vector Machine
Apron surveillance video coding based on compositing virtual reference frame with object library

Abstract A video coding framework for the apron surveillance scene has been proposed in this paper, which aims to improve coding efficiency by eliminating long‐term redundancy at the object level. To achieve this goal, this study first develops an existing block‐based hybrid video coding framework by exploiting the video redundancy on the object level to perform video coding. Second, an object‐library mechanism is designed to collect the representative object images as coding references on larger temporal and spatial scales. Finally, a virtual reference frame, which blends background and foreground references from the object library, is adaptively composited according to the video content to improve the inter‐prediction performance. Preliminary experimental results demonstrate that the proposed method achieves a high BD rate reduction of up to 23.97% in apron surveillance video sequences, compared to the standard high efficiency video coding (HEVC).