Reviewerly/exHarmony huggingface dataset:
Analysis of the Construction and Course Effectiveness of the Online Course Platform for College Piano in the Background of the Internet

Abstract Internet technology continues to develop and deepen, course learning methods present diversity, and piano courses are an important way for colleges and universities to carry out students’ musical quality training. In this paper, we first propose a behavioral sequence analysis method for predicting students’ online course performance in the context of the Internet for college piano online courses. The sequence-based prediction task is divided into two stages in total. In the first stage, the sequence characteristics of students’ online course learning behaviors are predicted by mining the potential expressions of the implicit layer through deep RNN. In the second stage, the support vector machine is used to create an optimal segmentation hyperplane. The SPC two-stage classifier is able to predict student course grades in order to identify students with learning crises in time. Then a layered MVC model is used to design the university piano online course platform system. Finally, the performance of the platform and the effectiveness of the course were tested. When the number of concurrent users reached 150, the success rate of the server in processing transactions remained at about 96%, and the pre-test and post-test of students’ piano scores showed that the mean value of score-reading skills was 34.32 in the pre-test and 45.63 in the post-test, with a mean value improvement of 11.31. The study showed that the online course client had high operational performance and the online course could effectively the study shows that the online lesson client has high performance and the online lessons is effective in improving the students’ piano performance.
Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion

Abstract To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map completeness of the traditional map building method with single-line LiDAR, this paper proposes a method to create fused raster maps realized with multi-source data. Firstly, the combined data of the inertial measurement unit and wheel encoder are corrected by adding the positional information output from the visual odometer using the error-state SLAM algorithm, and the local raster constructed by LiDAR and depth camera is fused frame by frame using the idea of Bayesian estimation to finally generate the fused global map. Then, a four-wheeled mobile unmanned vehicle with a LiDAR sensor and depth camera is selected as the experimental object, and dynamic environment avoidance simulation experiments are conducted to draw conclusions. The simulation experiment results show that when γ = 5.99, the algorithm generates a new local target point p g 2 (17.49, 13.49) and the corresponding getaway path and finally guides the unmanned vehicle to the specified target point, verifying that the method in this paper can achieve the avoidance capability of the unmanned vehicle in the process of getting away from the newly emerged obstacles. This study uses the scanned data of LiDAR for the estimation of the real-time position of the unmanned vehicle to realize obstacle avoidance and path planning of the unmanned vehicle.
A multivariate nonlinear dynamic programming of female figures in Qing dynasty literature

Abstract To improve the recognition of female images in Qing Dynasty literature, this paper designs a multivariate nonlinear-based image dynamic programming model. Data-driven typical correlation analysis is used to extract the key controllable variables with the strongest correlation with female image features and use them as input variables for modeling so as to make the maximum correlation between different image features and improve the modeling efficiency. The nonlinear spatial state model is established by using the subspace identification method, and the female image planning model is substituted into the multivariate nonlinear analysis, and the parameters of the nonlinear fitting function about the image planning can be obtained. The simulation analysis of the image dynamic planning model based on multivariate nonlinearity is carried out, and the results show that the average annual failure time of the model designed in this paper is 12.6 hours, and the classification accuracy in female image classification is 96.3%, and the classification time is stable at 1.162 s. The results show that the multivariate nonlinear features of the model can carry out dynamic planning of female images in Qing dynasty literature and improve the classification of image feature recognition.
Autonomous open-source electric wheelchair platform with internet-of-things and proportional-integral-derivative control

&lt;span lang="EN-US"&gt;This study aims to improve the working model of autonomous wheelchair navigation for disabled patients using the internet of things (IoT). A proportional-integral-derivative (PID) control algorithm is applied to the autonomous wheelchair to control movement based on position coordinates and orientation provided by the global positioning system (GPS) and digital compass sensor. This system is controlled through the IoT system, which can be operated from a web browser. Autonomous wheelchairs are handled using a waypoint algorithm; ESP8266 is used as a microcontroller unit that acts as a bridge for transmitting data obtained by sensors and controlling the direct current (DC) motors as actuators. The proposed system and the autonomous wheelchair performance gave satisfactory results with a longitude and latitude error of 1.1 meters to 4.5 meters. This error is obtained because of the limitations of GPS with the type of Ublox Neo-M8N. As a starting point for further research, a mathematical model of a wheelchair was created, and pure pursuit control algorithm was used to simulate the movement. An open-source autonomous IoT platform for electric wheelchairs has been successfully created; this platform can help nurses and caretakers.&lt;/span&gt;
Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records

Background: Using deep learning for disease outcome prediction is an approach that has made large advances in recent years. Notwithstanding its excellent performance, clinicians are also interested in learning how input affects prediction. Clinical validation of explainable deep learning models is also as yet unexplored. This study aims to evaluate the performance of Deep SHapley Additive exPlanations (D-SHAP) model in accurately identifying the diagnosis code associated with the highest mortality risk. Methods: Incidences of at least one in-hospital cardiac arrest (IHCA) for 168,693 patients as well as 1,569,478 clinical records were extracted from Taiwan’s National Health Insurance Research Database. We propose a D-SHAP model to provide insights into deep learning model predictions. We trained a deep learning model to predict the 30-day mortality likelihoods of IHCA patients and used D-SHAP to see how the diagnosis codes affected the model’s predictions. Physicians were asked to annotate a cardiac arrest dataset and provide expert opinions, which we used to validate our proposed method. A 1-to-4-point annotation of each record (current decision) along with four previous records (historical decision) was used to validate the current and historical D-SHAP values. Results: A subset consisting of 402 patients with at least one cardiac arrest record was randomly selected from the IHCA cohort. The median age was 72 years, with mean and standard deviation of 69 ± 17 years. Results indicated that D-SHAP can identify the cause of mortality based on the diagnosis codes. The top five most important diagnosis codes, namely respiratory failure, sepsis, pneumonia, shock, and acute kidney injury were consistent with the physician’s opinion. Some diagnoses, such as urinary tract infection, showed a discrepancy between D-SHAP and clinical judgment due to the lower frequency of the disease and its occurrence in combination with other comorbidities. Conclusions: The D-SHAP framework was found to be an effective tool to explain deep neural networks and identify most of the important diagnoses for predicting patients’ 30-day mortality. However, physicians should always carefully consider the structure of the original database and underlying pathophysiology.
Dual Construction of “Composition” and “Analysis” in Composition and Technical Theory in the Context of Big Data

Abstract Based on big data technology, this paper firstly collects and pre-processes educational resources and builds a dual teaching system based on two basic concepts of “creation” and “analysis”, combining technology and theory. Secondly, the FCM clustering algorithm is used to cluster the student learning data and get the optimal clustering center, and the Lagrange multiplier method is used to optimize the objective function, identify the student learning characteristics, and conduct a dynamic pre-testing assessment. Finally, the quantum optimization algorithm dynamically assigns the assessment data to eliminate redundant data and construct a feature set library for individual student learning. The practical analysis and application results show that the effect feedback in the two courses of composition and orchestration reached 88% and 93%. In the music analysis and music composition assessments, the percentage of the number of students with performance range values of 100-90 averaged 43% and 42.75%, respectively. In addition, 72.97% of the students found the system helpful in independent learning ability. It indicates that the teaching system constructed in this paper can better serve the teaching of composition technique and further improve the teaching model and process.
Teaching Strategies of English Speaking Informatization in Colleges and Universities in the Context of Computer-Assisted Language Learning

Abstract In order to improve the quality of teaching spoken English, this paper reconstructs the semantic structure through the weight matrix, and the association matrix describes the mapping process in the context of computer-assisted language learning. Pooling layer vectors are computed, and translation transformation matrices are encoded in real time so as to extract language vector features. Feature regression evaluation is implemented in a restricted Boltzmann machine to enhance the decoding language target through human-computer interaction, described using nonlinear functions, and the text to be translated is input to the deeply structured semantics, which is processed by weighted averaging to form a spatial vector to complete the input layer mapping process for the utterance. To verify the feasibility of this teaching model, the effectiveness of the teaching strategy is analyzed. The results showed that the variance of spoken English of college students with computer assistance was 0.534, the memory rate increased by at least 0.07, and the value of the number of words memorized by college students increased by more than 98.72%. It can be seen that the proposed information-based teaching model of spoken English in colleges and universities breaks the traditional education method and makes the teaching strategy approach more diversified.
Deep learning model-based reading of Chinese language and literature classics and situational experience

Abstract In this paper, a new media scenario experience model is constructed to integrate Chinese language and literature works through deep learning algorithms. Firstly, the model is trained as a whole, RBM is a special form of Boltzmann machine, and the energy function is defined by combining the input layer vector and the hidden layer vector to calculate the marginal probability distribution. Then the defined energy function can be used to derive the joint probability formula of state values, link this probability formula with the hidden layer between the neuron nodes, use the logistic function to define the energy of the whole Boltzmann machine, and finally, after repeated training to derive the entropy function of the RBM, to complete the construction of a new media-based scenario experience platform. The experimental results show that after a semester of teaching based on this platform, the scoring examination of new media scenario experience classical reading was conducted. The percentage of those who passed was 82%, and only 18% failed. Therefore, we should make full use of multimedia technology, innovate efficient Chinese language and literature teaching methods, turn the classroom into a stage, enhance the novelty of Chinese language and literature courses, and enhance the intuitiveness of classical reading in Chinese language and literature.
Analysis of the Evolution Trend of Segmentation of Online Public Opinion Based on Dynamic Tracking Technology

Abstract In this paper, the dynamic tracking technique is applied to analyze the triggering factors of online public opinion, modify the feature subsets and establish a basic database of sensitive information. The test of statistical characteristics of the evolution process of public opinion is realized by using the prediction of time series by the ANOVA algorithm, and on this basis, the smoothness of the model is realized by testing the parameters. In the process of establishing the quantitative index system, the seasonal difference algorithm is introduced to predict the development trend of public opinion on hot topics to realize the visualization model design. Finally, in order to verify the validity of the model, an experiment was conducted with Event A as an example. The results show that users can set a total of 4 keywords with an average word frequency of 0.03 Hz. The prediction accuracy in different periods can reach 88.73% on average, and the adaptation value can reach 0.8 percentage points on average. Thus, it can be seen that the trend analysis model of online public opinion evolution constructed in this paper can quantitatively analyze the mechanism of public opinion segmentation evolution and the trend prediction problem and provide a theoretical reference for the government to govern public opinion.
Towards fostering the role of 5G networks in the field of digital health

&lt;div align="center"&gt;&lt;span lang="EN-US"&gt;A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future.&lt;/span&gt;&lt;/div&gt;
The introduction and cultivation of intercultural communication in undergraduate German language teaching process based on big data technology

Abstract In this paper, we first obtain the most representative subset of learning behavior features from big data and evaluate each feature value using a genetic algorithm to obtain its weight parameter. Then a similar least squares method is used to establish behavioral performance indicators through temporality, and the loss function is used to classify the indicators to obtain accurate teaching parameters. Finally, the K-means algorithm is used to mine the spacing distance of different log data and obtain the corresponding temporal feature vector to the learning behavior data with similarity. The application effectiveness of this teaching platform was tested, and the results showed that the propagation time of the big data platform takes only 112ms, the start-up time is 158ms, and the highest access number ratio can reach 0.82. The passing rate of students using this platform is as high as 73% on average, and the average length of independent learning reaches 6.5 hours. It shows that the online German teaching platform built on the basis of big-number technology has cultivated students’ international vision and cross-cultural communication skills.
Research on digital reform of music education resources in universities in the context of data governance

Abstract This paper proposes the use of the Hadoop-distributed file system HDFS as a storage system for large resources in music education. In order to improve the overall performance of HDFS for storing educational resources and to solve the deficiencies of the current system. This paper provides an overall analysis of the current state of research related to the placement algorithm and data storage strategy of HDFS, respectively. After combining the characteristics of educational resources themselves and establishing an educational resource value evaluation index system, an improved placement algorithm and a hybrid redundant storage strategy based on educational resource ratings are proposed, and the proposed algorithm or strategy is simulated and implemented and verified through source code analysis of HDFS-related algorithms. The results show that the average response time for uploading 10 songs is 7.734 s, and the average response time for image retrieval is 0.768 s. In the analysis of digital music education resources demand, teachers have the highest usage rate of music teaching theory (M=3.43), 38.45% of students’ demand is mainly for downloading music, and music lovers querying popular music has the highest percentage of 19.43%. The algorithm in this paper achieves the storage load balance of each node, which basically satisfies the demand for music education resources of teachers and students in universities.
Visual learning analysis of physical virtual simulation experiments based on heterogeneous data features

Abstract In order to provide a way to develop the teaching effectiveness of physics experiments, this paper optimizes the platform search engine by combining heterogeneous data features and representing document information as feature vectors based on visual learning analysis methods. The algorithm is dynamically adjusted according to the authority to build a network database. And the virtual physics experiments have interacted with virtual experimental equipment to build a physics virtual simulation experiment platform. The results show that the overall level of visual student portrait analysis is above 40%, and the average completion efficiency of visual evaluation tasks 1-9 is 87.9%, which helps the digital transformation and upgrading of experimental physics teaching and promotes the construction of high-quality virtual simulation experimental teaching system.
A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept mapping with transformer

&lt;span lang="EN-US"&gt;Automatic text summarization generates a summary that contains sentences reflecting the essential and relevant information of the original documents. Extractive summarization requires semantic understanding, while abstractive summarization requires a better intermediate text representation. This paper proposes a hybrid approach for generating text summaries that combine extractive and abstractive methods. To improve the semantic understanding of the model, we propose two novel extractive methods: semantic latent Dirichlet allocation (semantic LDA) and sentence concept mapping. We then generate an intermediate summary by applying our proposed sentence ranking algorithm over the sentence concept mapping. This intermediate summary is input to a transformer-based abstractive model fine-tuned with a multi-head attention mechanism. Our experimental results demonstrate that the proposed hybrid model generates coherent summaries using the intermediate extractive summary covering semantics. As we increase the concepts and number of words in the summary the rouge scores are improved for precision and F1 scores in our proposed model.&lt;/span&gt;
The significance of senior education in the Internet era for the construction of lifelong education system

Abstract This paper uses Bayesian networks for the state combination of random variables to describe the relationship between prior probability and posterior probability by likelihood. Meanwhile, the elimination of complex variables is achieved by combining the operation results of multivariate non-independent joint conditional probability decomposition. Based on this, Bayes’ theorem is used to calculate the learning efficiency, the comprehensive pass rate of any interactive courseware, and the growing value of the visualized learning process to improve the sharing of resources for senior education. To verify the feasibility of the model, a teaching effectiveness test was conducted, and the results showed that 66.6% of older adults believed that participation in senior education could strengthen their bodies and minds. The percentage of medical and health care knowledge courses was 63.20%. The direct effect of the learning environment on the participation behavior of senior education was 0.143, while the indirect effect of educational resources on the participation behavior of senior education was 0.083. In addition, 61.78% of seniors thought that attitude towards senior education was very important. Therefore, it can be concluded that the model of the elderly education system fills the gap in the structure of the lifelong education system, enriches the uppermost structure of the lifelong education system, and promotes the improvement and development of the lifelong education system.
Pengaruh Sistem Penataan Ruang Terhadap Minat Baca Siswa Di SMPN 1 Sungai Tarab

The purpose of this study was to find out how the library spatial planning system at SMPN 1 Sungai Tarab works. The layout of the school library is the arrangement or arrangement of all school library facilities in the available space or building. Where by paying attention to the library spatial arrangement system in the school the writer was able to find out how much interest is in reading at SMPN 1 Sungai Tarab where interest in reading is a potential to read voluntarily.