Reviewerly/exHarmony huggingface dataset:
ANALISIS PENGARUH BANTUAN SOSIAL PEMERINTAH DAN LITERASI KEUANGAN TERHADAP MAHASISWA YANG TERDAMPAK COVID-19

This Studies aims to examine the analysis of the effect of government social assistance and financial literacy on students affected by COVID-19. This research is a policy research. The method used in this research is descriptive qualitative with evaluative data and literature studies. In this study, literature was obtained from information that developed in the news related to the distribution of government social assistance and financial literacy information circulating among Telkom University students affected by Covid-19.
ConvLSTM for Vehicle Localization in Seismic Sensor Networks

Localization of moving military vehicles plays a vital role for border security and safeguarding high-security facilities. Commonly applied range-based localization techniques such as time of arrival, time difference of arrival, angle of arrival, and received signal strength rely on known transmitters. However, when seismic sensor networks are used for localization of moving targets, where moving targets can be treated as unknown transmitters. In this work, we consider a scenario where only receivers are deployed to perceive seismic signals transmitted by the moving military vehicles with unknown locations. Consequently, conventional closed-form equations for distance-based trilateration are not applicable. To address this challenge, we present a novel approach for accurate localization. Our method involves clustering closely deployed sensor nodes to effectively fuse their information to estimate the positions of the moving military vehicles. We leverage multiple-input convolutional neural networks, utilizing one input to represent the short-time discrete Fourier transform of signals from each node, and another input to encode the relative locations of sensors within clusters. Through extensive experimentation, we demonstrate that our proposed method significantly reduces localization errors when compared to existing distributed regression methods.
A Study on the Utilization of ChatGPT to Secure Competitiveness of the Banking Sector

Recently, the banking industry has been implementing AI bankers utilizing artificial intelligence on-site. However, the application of AI bankers has revealed various technical issues, prompting efforts by banks to address and improve them. In this paper, we propose the combination of AI bankers and ChatGPT as one method of improvement. It is anticipated that this combination will alleviate some of the identified issues and bring about diverse changes in banking services. Specifically, we suggest enabling AI bankers to handle customer interactions by leveraging ChatGPT's ability to store and recall previous conversation context. Additionally, we propose expanding the scope of AI bankers by combining them with ChatGPT trained on regional dialects to recognize and incorporate regional expressions. This approach is expected to enhance productivity and efficiency by bankers focusing on other core tasks. Moreover, the introduction of ChatGPT for employees is expected to improve the quality of banking services through accurate task processing, ultimately increasing customer satisfaction.
AI tuning can lead to rule breaking

OpenAI's developer tool for its GPT-4 large language model can remove the safety measures
Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces

Jointly optimising both the body and brain of a robot is known to be a challenging task, especially when attempting to evolve designs in simulation that will subsequently be built in the real world. To address this, it is increasingly common to combine evolution with a learning algorithm that can either improve the inherited controllers of new offspring to fine tune them to the new body design or learn them from scratch. In this paper an approach is proposed in which a robot is specified indirectly by two compositional pattern producing networks (CPPN) encoded in a single genome, one which encodes the brain and the other the body. The body part of the genome is evolved using an evolutionary algorithm (EA), with an individual learning algorithm (also an EA) applied to the inherited controller to improve it. The goal of this paper is to determine how to utilise the results of learning process most effectively to improve task performance of the robot. Specifically, three variants are investigated: (1) evolution of the body+controller only; (2) a learning algorithm is applied to the inherited controller with the learned fitness assigned to the genome; (3) learning is applied and the genome is updated with the learned controller, as well as being assigned the learned fitness. Experiments are performed in three different scenarios chosen to favour different bodies and locomotion patterns. It is shown that better performance can be obtained using learning but only if the learned controller is inherited by the offspring.
Hematology Analyzers and Reagents

A hematology analyzer mainly detects formed elements in blood, including red blood cells (RBCs), platelets, white blood cells (WBCs), and hemoglobin in RBCs. Such detection can be accomplished with three main categories of techniques: electrical impedance, light scattering, and colorimetry, each including several subcategories. Here is a brief overview of them.
FORMATION OF COMPUTATIONAL SKILLS OF STUDENTS USING INFORMATION TECHNOLOGIES

The article presents a methodology for the formation of cognitive, computational skills, educational activities of younger students with the help of information technology. The role of information technologies in the formation of students’ computing skills has been clarified. The article considers the circumstances of the formation of fast counting skills in students using Internet applications in mathematics lessons. The article describes how to increase students’ interest in the subject by offering level tasks given in the state educational standards.
REAL WORLD APPLICATIONS AND RESEARCH DIRECTIONS FOR MACHINE LEARNING: CHALLENGES AND DEFIES

The digital world offers a lot of data in this era of the Fourth Industrial Revolution. Intelligently analysing these data and implementing suitable smart and automated applications at scale. Machine learning refers to a set of algorithms, scientific investigations, and statistical models that allow computers to accomplish tasks without having to be explicitly programmed. Many of the applications we use on a daily basis involve learning algorithms. The main advantages of using machine learning are; once an algorithm learns what to do with data, it can do its work automatically. In this paper, we present a comprehensive view on machine learning algorithms that can applied to Enhance the intelligence, the techniques and their applicability thus, this study’s key contribution is explaining the principles of different machine learning techniques application domains such as cyber security systems, smart cities, health-care, e-commerce, agriculture and many more as for decision makers in various real world situation and application area from technical point of view.
Research on the Application of Traditional Sports Culture in College Physical Education Teaching under the Background of Deep Learning

Abstract In this paper, the transformer target detection algorithm is established by combining deep learning technology, and the Deep algorithm is added on the basis of SORT to optimize and improve SORT and overcome the problems of SORT. After the DeepSORT model is constructed, YOLOv5 is introduced into the model, and finally, the YOLOv5-DeepSORT model for sports element detection and tracking is constructed and completed. The ATT-BERT sports culture element entity recognition model is constructed from four levels: the BERT layer, the BiLSTM layer, the attention layer, and the CRF layer, which results in an application framework for the integration of traditional sports culture into teaching. The influence factors of sports culture perception and sports teaching, as well as the integration of traditional ethnic sports culture into sports teaching in Z, were empirically examined. The study shows that the correlation coefficient between the two is around 0.465, and there is a positive and strong correlation between the two. The correlation coefficients of the dimensions for physical education students and non-physical education students were 0.513, 0.483, 0.485, and 0.462, respectively. The most common teaching methods used by teachers in physical education classrooms integrating traditional sports culture were explanation, demonstration, and complete decomposition, with ratios of 0.78 and 0.7, respectively. The integration of sports culture into physical education has resulted in good results. The outcome was satisfactory.
A Blended Instructional Design for Civil Litigation Combining Multi-Objective Optimization Algorithms in a Rule of Law Context

Abstract The current traditional means of teaching adopt the indoctrination teaching mode. Students passively accept information, learning enthusiasm is not high, and the traditional teaching mode needs to be reconstructed urgently. This paper constructs a blended teaching mode for civil litigation based on the WeChat public platform and provides the implementation process for the blended teaching mode. In order to enable students to obtain civil litigation-related resources more in line with the learning characteristics of the WeChat platform, a multi-objective learning path optimization algorithm is proposed, and the discrete binary particle swarm algorithm is used to optimize and solve the multi-objective learning path. The teaching experiment method is utilized to select the research object and method to study the teaching effect of the blended teaching mode and then obtain the students’ satisfaction with the blended teaching mode. The results show that through the BPSO algorithm, after the number of iterations reaches 150 times, its fitness value is around 10-5, the average running time of the 20 test functions is around 500ms, and the mean value of students’ satisfaction with the blended teaching mode is 9.31 points. The blended teaching mode of civil litigation in the context of the rule of law can help students better establish the thinking of the rule of law and promote the improvement of students’ learning ability, and multi-objective optimization can help students better obtain the optimal learning path.
Analysis of Strategies for Enhancing Informatization Teaching Ability of College Teachers Combined with Mathematical Statistics Multiple Regression Models

Abstract In this paper, from the perspective of education informatization, for the problem of teachers’ informatization teaching ability in colleges and universities, the loss function of the analysis of teachers’ informatization teaching ability in colleges and universities can be calculated by using a Logistic regression algorithm. In order to prevent the overfitting problem of multiple regression analysis, it is necessary to optimize the multiple regression analysis by regular transformation, and then construct the model of teachers’ informatization teaching ability based on multiple linear regression, and determine the research variables and regression equations in the model. Starting from the research objectives, the research objects and problems are determined, and two analysis methods, simulation analysis and statistical analysis, are used to empirically analyze the informatization teaching ability of college teachers. The results show that on the analysis of model instances, the correlation coefficients between the factors and the overall teacher educators’ informatization teaching ability range from 0.671 to 0.829, and there is a certain correlation between each factor and teacher educators’ informatization teaching ability. On the analysis of differences in teachers’ informatized teaching competence, the five competence constructs of Guangzhou college teachers’ education in technological literacy (P=0.653&gt;0.05), instructional design (P=0.133&gt;0.05), instructional implementation (P=0.584&gt;0.05), evaluation development (P=0.287&gt;0.05), and informatized teaching competence (P=0.413&gt;0.05) do not significant differences exist. This study comprehensively analyzes the strategies for improving teachers’ informatization teaching ability in order to play some role in promoting the development of education informatization.
Research on the Era Value and Communication Path of Human Destiny Community and Socialist Core Values in the Context of Big Data

Abstract This paper analyzes the socialist core values and explores the contemporary characteristics and values of socialist core values under the background of big data. On this basis, in order to better practice the socialist core values, through the communication path of big data, combined with the belief propagation algorithm and the association effect, it constructs a node influence propagation NIP model based on the improvement of knowledge graph to realize the propagation of socialist core values. At the same time, the algorithm constructed in this paper is subjected to performance verification experiments, and the effect of socialist core value dissemination and the value of the times are explored. The results show that the influence range of the national level and the individual level are roughly the same as 1200 and 1190, respectively, both slightly higher than the individual influence range of 1000, and the propagation time of the state, society and individual is within 150s, 200s and 300s respectively. Resistance to Western ideology rose by 0.13, the deepening of socialist theory grew by 0.09, and the process of constructing a world value system increased by 0.06. Based on this paper, the study of the value of time and the path of dissemination has a positive impact on the development of socialism.
An analysis of the application of computer-assisted instruction in the teaching of physical education yoga

Abstract In this paper, according to the TOF method in the principle of Kinect V2.0 depth measurement, the Kinect data stream can be obtained, which can realize the real-time acquisition of the user’s yoga movement data. On the basis of the extraction of joint motion features, the position of each joint point in the model under the model coordinate system is calculated sequentially using the action redirection algorithm. For the intermittent problem of keyframes in the yoga action screen of the joints, it is necessary to optimize the analysis of yoga action recognition through the action keyframe interpolation method. Starting from the demand for sports yoga teaching, we determine the design scheme of a computer-aided yoga teaching system, realize the design of a computer-aided yoga teaching system based on the system development platform and programming program, and test the performance of the computer-aided yoga teaching system by using simulation analysis. The results show that the yoga movement redirection algorithm, when matching the data information of the same movement, the distance value is 1 to 2 orders of magnitude smaller compared to the matching distance between different yoga movement sequences, and the use of the joint angle value can find out the joints where the trainer’s movement is not up to the standard, and the yoga trainer can reasonably adjust the movement based on the similarity and the joint angle value given by the system to achieve the purpose of yoga training.
A study on the application of artificial intelligence in the design of intelligent medical robots

Abstract With the development of artificial intelligence and robotics technology, the combination of artificial intelligence and medical device research and development has been promoted, which is an important product of the development of artificial intelligence. In this paper, the general structure of the intelligent medical robot is designed by combining artificial intelligence technology and robotics-related technology. Then, the binocular vision function of the robot was realized by visually acquiring the image of the target object, 3D reconstruction of the target object, and combining the SIFT image recognition algorithm and target tracking algorithm. Then, a new speech recognition algorithm was constructed to realize the human-robot interaction function with the medical robot based on the deep learning Transforme network after the construction of the human acoustic model. Finally, the designed intelligent medical robot was tested, and its overall performance was evaluated. The results show that the recognition errors of the intelligent medical robot on the features of the items are all within 0.05, the recognition errors on the features of the human body are within 0.2, and the speed of the target tracking is between 6km/h and 16km/h. The average recognition accuracy of the medical robot for voice commands is about 0.9, the recognition time is about 0.7s, the normal working rate of each function is more than 0.99, and the test speed is within 2s.
The application of immersive multimedia information technology in the teaching of vocal music

Abstract In this paper, immersive multimedia information technology is utilized to perform frame-splitting and window-adding operations on vocal signals in college vocal music teaching to facilitate the extraction of vocal signal features. According to the Principal Component Analysis feature space projection and Relief feature selection, the vocal emotion regression model combining multimedia information technology is constructed, and the structure of the vocal emotion regression model and its teaching application are explored in the vocal emotion regression model. Determine the research object and method, according to the research program on the integration of multimedia information technology of vocal music teaching research design and implementation, and the use of statistical analysis of multimedia information technology-based vocal music teaching empirical analysis. The results show that the subjects had the best emotional experience with an emotional immersion degree of 5.651 when the music of the calm category and the visual music motion picture of the calm category constituted the visual music and the vocal music immersion based on the vocal music emotional regression model of “combining multimedia information technology” was significantly enhanced when the emotional type of the music and the emotional type of the dynamic picture of the visual music was the same. This study carries out college vocal music teaching based on students’ emotional characteristics, which is of great significance to improve the quality of college vocal music teaching and students’ musical literacy.
Knowledge Graph-based Interactive Sharing Platform for Teaching English Majors in Colleges and Universities

Abstract In this paper, we found that the traditional FCM clustering algorithm is over-sensitive to noise or outliers, and AP clustering is added on the basis of the FCM algorithm to improve and optimize the FCM algorithm. The improved and optimized algorithm is used to derive high-frequency words for teaching English majors, and the hot knowledge map of English teaching is studied according to the high-frequency words. Construct the knowledge map framework and utilize the knowledge map framework to build the U+ English teaching interactive sharing platform. Adopt a top-down approach to construct the knowledge graph, and after defining entity relationships and attributes, propose the learning path recommendation method of the U+ platform based on the knowledge graph. Comparative experiments are used to analyze the teaching effect of the U+ platform and students’ English learning engagement. After 8 weeks of interactive and shared learning on the U+ platform, the students in the experimental class improved their English single choice, perfect fill-in-the-blank, and reading comprehension scores by 2, 4, and 3 points, respectively, and the U+ platform had obvious help in improving the students’ English scores, and the platform was effective in teaching. The mean value of the positive dimension of learning engagement of the students in the experimental class is 4.1634, with an interval range of [4,4.3], and they are positively engaged in English learning, and the mean value of the negative dimension is 1.8341, with a range of [1.6,2.4], and there are fewer negative performances, and the U+ platform has a positive impact on the student’s English learning engagement.
Research on the Innovation of Talent Cultivation Mode and Industry-Education Integration Mechanism of College Education in the Internet Era

Abstract In this paper, in order to obtain a better classification evaluation effect, a feedback connection model is added to the convolutional neural network to establish the evaluation model of the integration of industry and education in colleges and universities based on FCNN. Compare the MSE loss values of the traditional BP neural network model and the FCNN model. Indicator system construction, with the help of the accuracy of the convolutional neural network, to carry out the whole process of evaluation around the indicators, weights, and the quality of the implementation results. The data of students’ micro-expression concentration recognition test is used as the evaluation data of students’ project participation, comparing the recognition rate of the participation evaluation system proposed in this paper and the traditional participation evaluation system to complete the quality evaluation of the talent cultivation model of college education. Analyze the data on the graduation rates of college graduates to determine the effectiveness of the university’s integration of college education. The analysis shows that in 2022, the professional matching employment rate of graduates was 86.28%, which reflects the high efficiency of the university’s industry-teaching integration on the cultivation of professional and applied talents, and the mechanism of industry-teaching integration is well affiliated.
The Application of Digital Multimedia Technology in the Innovative Mode of English Teaching in institutions of higher education

Abstract Innovative English teaching resources will inevitably result from the use of media technology in the classroom. This article will examine the new approach to teaching English in college from the standpoint of teaching resources. In order to establish a new mode of English teaching in higher education institutions based on personalized recommendation of teaching resources, this paper proposes two recommendation algorithms: one is content-based, and the other is collaborative filtering. The algorithm is enhanced by combining it with the design of multimedia English teaching resources. The influence of hit rate and MAE values prior to and following the cooperative filtering recommendation method is examined. The distinction between the pre-and post-test results of the experimental and control courses is analyzed and compared to conclude the examination of the impact of the novel model of teaching college English. The analysis indicates that the improved recommendation algorithm has a 4.85% higher hit rate than the traditional collaborative filtering algorithm. The improved recommendation algorithm is more accurate than the traditional recommendation algorithm, as suggested by this.