Reviewerly/exHarmony huggingface dataset:
Research on the Development of Young Teachers’ Informatization Teaching Ability in Universities Based on Big Data Technology

Abstract This paper presents an incomplete multi categorization algorithm based on relative distance, which instead of simply using the Euclidean distance between class centers to measure the degree of dissimilarity between classes, uses the relative distance between classes to measure the dissimilarity of classes. The entire algorithm strictly follows the principle of splitting the most easily separable classes first, which decreases the accumulation of errors occurring in the binomial tree structure and improves the algorithm’s overall evaluation accuracy. Relevant tests are designed to measure the comprehensive performance of the algorithm. In terms of accuracy, the algorithm in this paper can be 4.14% higher than the best OVR algorithm and 1.63% higher than the best OVO. The experimental results prove that the algorithm is effective and that the overall time consumption in testing time is less than OVR and OVO. The analysis of young teachers’ informatization teaching ability obtained that the main influencing factors were the lack of theoretical guidance for teachers’ teaching, the lack of a common sharing platform for subject teaching resources and the lack of a sound service support system, which influenced the informatization teaching ability to 86%, 83.62% and 84.06%, respectively. Therefore, the results proved that the algorithm is effective, young teachers should focus on theoretical training in teaching informatization, and the school should build an informatization teaching platform to improve the service support system.
Analysis of media content recommendation in the new media era considering scene clustering algorithm

Abstract With the advent of the new media era, we face the problem of information overload every day, based on which this paper proposes a scenario-conscious clustering algorithm for recommending media content. The improved K-means algorithm is used to cluster the media content, an initial cluster center is randomly selected, and the remaining initial cluster centers are obtained by executing the improved algorithm to reduce the number of iterations and avoid neighboring situations. The clustering algorithm is then compared with content-based recommendation techniques, KNN recommendation algorithm, and collaborative filtering recommendation algorithm in terms of accuracy, recall, MAE value, and execution time. The clustering algorithm is significantly better than the other algorithms regarding accuracy and recall. The accuracy of the clustering algorithm is 0.4 when the recommendation sequence is 30, which is 0.4 higher than the collaborative filtering technique, 0.1 higher than the KNN, and 0.12 higher than the content recommendation method. In terms of MAE value, the clustering algorithm outperforms the other algorithms when the number of nearest neighbors is selected to be above 20. In terms of execution time, the longer the amount of data, the more obvious the advantage of the clustering algorithm. Therefore, the applicability and reliability of the model proposed in this paper for media content recommendation are verified in terms of accuracy, recall and execution time, which meet the design requirements.
The application of computer VR technology in physical education

Abstract Under the digitalization of big data information, how to use VR technology combined with physical education teaching is a hot topic of concern in the current education sector. Firstly, the intelligent teaching system of VR technology based on genetic algorithms is proposed based on big data technology. Secondly, the system framework and ideas are constructed according to the needs of teachers and students in physical education, and then the debugging and operation of the teaching system are realized. Finally, to ensure more accurate experimental results of VR technology applied in college physical education, the homogeneity of experimental objects is detected through a VR technology intelligent teaching system based on analyzing efficiency values in the application of VR technology in physical education. The results show that the average efficiency values of VR technology in basketball, soccer, volleyball, badminton and table tennis are 21.04%, 19.36%, 19.64%, 20.33% and 19.63%, respectively. This study verifies that VR technology is beneficial to improving physical education teaching efficiency and provides a guiding reference value for improving physical education teaching in colleges and universities.
Public Speaking Training for Youth in Koto Perambahan Village, Kampar Regency in Improving Public Speaking Skills

The ability to speak in public (Public Speaking) for a teenager is one of the abilities that greatly supports activities in working and doing various creativity. Especially in the current era of technological advancement, there are many things that can be done by teenagers as an effort to make breakthrough changes in a positive direction by mastering Public Speaking. It's just that the fact that many teenagers do not have good public speaking skills to convey ideas in public so that it hampers their efforts in this regard. The purpose of this activity is to uncover potential as well as an effort to help Koto Perambahan Village teenagers in improving their skills in the field of Public Speaking or public speaking skills. Public speaking skills are something that must be trained from an early age; Because it will be beneficial for the future. The method of implementing service consists of preparation, implementation and evaluation. The result of this activity is that the trainee teenagers have demonstrated their Public Speaking skills after getting direction and guidance.
Gymnastic movement recognition based on support vector machine classification model

Abstract The gymnastic movement recognition system is designed to determine the standard degree of trainers’ movements by accurately grasping their body contours and body positions through motion capture to achieve the training effect. This paper proposes an improved LSI-SVM algorithm based on a support vector machine applied to a gymnastic movement recognition system. Firstly, based on the idea of NMFDA, for each class of samples, its intra-class k-nearest neighbor and inter-class k-nearest neighbor local structure information are mined. Secondly, the obtained structural information is introduced into TSVM to obtain a new classification model. Finally, system operation test experiments are conducted to verify the generalization and accuracy of the proposed LSI-TSVM algorithm for the gymnastic movement recognition system. The experimental results show that the average recognition rate of the LST-TSVM algorithm proposed in this paper is 94.1%, which is 5.9% higher than S-TSVM and 14% higher than SVM, and 8.9% higher compared to SRSVM. The gymnastic action recognition system based on the LST-TSVM algorithm can effectively solve the problem of matching the corresponding frames of gymnastic action sequences on the time axis and effectively improve the action recognition rate, which can better assist trainers in learning gymnastic items.
Language Distance and L3 Japanese Acquisition in Morphosyntactic Module

This study applies mathematical linguistics to explore how language distance plays an essential role in third language acquisition in terms of a morphosyntactic module. Data were drawn from 3410 essays written in Japanese by low, middle and high levels of learners from 12 first-language (L1) backgrounds who acquire English as a second language (L2)-interlanguage and Japanese as a third language (L3). The findings indicate that (a) mean dependency distance is an efficient indicator for syntactic complexity of writing proficiency. In both elementary and intermediate groups, learners of highly agglutinative languages are likely to show higher dependency distance than learners from isolated-language and fusionlanguage backgrounds. (b) The frequency and dependency distance are distributed in Power Law Function. Fitting Right truncated Good to the dependency distances indicates that the values of the parameter p ascend as the degree of agglutination of learners’ mother tongue increases. (c) The syntactic complexity in multi-background Japanese learners’ essays highlights that no matter how diverse the learners’ native and target languages are, the syntax is always constrained by universal law, namely, minimising dependency distance. This is in accordance with existing findings in second language acquisition of inflectional languages.
The Innovation and Application of Computer Multimedia-assisted Teaching Methods in College English Teaching

Abstract The multimedia-assisted teaching model has entered the practice of English teaching (ET) in colleges and universities and has greatly influenced English teaching. In this paper, we conduct an empirical study on multimedia-assisted English teaching. Firstly, we propose a neural machine translation model based on multi-granularity features combined with dynamic word vectors to improve the problem of inaccurate English translation. Secondly, we propose a grammatical error correction model based on a generative adversarial network to correct grammatical errors in teaching English. The experimental results show that the improved model obtains 35.58 and 45.71 BLEU values in the experiments by comparing the neural machine translation models, and the translation accuracy is improved by 23.9% compared with the traditional model. In the experiments through multimedia-assisted teaching, the multimedia-assisted teaching model improved student performance by 9.8%, while the traditional teaching model improved by only 1.9%. The multimedia-assisted teaching model proposed in this paper has a positive effect on students’ performance, intercultural communication (IC) awareness, learning initiative, and interactivity, and also provides a valuable reference for multimedia-assisted language teaching.
Research on the organization of public space relations in commercial complex buildings in the context of big data

Abstract As an emerging type of large commercial building, commercial complexes play an increasingly important role in contemporary urban development and construction trends. In order to better study the organization of public space relations in commercial complex buildings, this paper selects eight commercial complexes as sample cases to analyze their spatial design in terms of both content public space and external public space based on big data technology. Regarding the design of the commercial complexes’ internal public space movement, the highest accessibility degree reached 95.3%, while the average was 82.5%. Regarding visibility, the highest level was 96.0%, and the average was 83.3%. For large complexes, better spatial relationships can be designed to improve the circulation degree and thus enhance customers’ consumption experience. Regarding the convenience of traffic connection to the external space of commercial complexes, an average of 41.4% of customers consider it very convenient, 23.1% consider it relatively convenient, 27.9% consider it somewhat inconvenient, and 18.0% consider it very inconvenient. Commercial complexes can effectively attract more repeat customers if they can improve the convenient design of transportation connections. The study of public relations of commercial complexes based on big data helps to discover the defects of their public space design and plays a significant role in optimizing spatial design and relationship organization and strengthening their integrated functions.
Research on Fault Prevention and Maintenance System of Automatic Substation Primary Equipment Based on Decision Tree Algorithm

Abstract Electric power enterprises are developing rapidly in the era of big data information digitization. At this stage, the total number of substations is gradually increasing, the structure of the power engineering system is slowly becoming complicated, and the video monitoring system instantly collects a lot and contains a lot of noisy data information, which affects the power supply system’s access to effective data information and fault detection. To prevent the above phenomenon. This paper selects a decision tree algorithm to obtain and analyze meaningful operation-confirming information from a large amount of data information, and then can quickly and confirm the diagnosis of common fault machines and equipment in substations, reduce the running time of common fault machines, and improve the safety and reliability of primary equipment in substations with automation technology. The paper describes the basic concept of big data mining common algorithm and its data mining algorithm in the automation technology substation primary equipment fault detection, selected the typical alarm signal to start the analysis, and categorization and collocation solution. A decision tree algorithm entity model is built, several classical decision tree algorithms are described, and their data analysis is carried out for each attribute, and then the decision tree algorithm is improved. According to build the decision tree algorithm according to improve the decision tree algorithm under the fuzzy set base theory, mainly by expertise in the four on cannot identify the association, rough set and up close and down close, similar and membership relationship, expertise concise to optimize the calculation method. And the common ID3, C4.5, and CRAT algorithm of each property is compared and analyzed, and the results show that: compared with C4.5 and ID3, the boosted optimization algorithm has higher classification accuracy and can model rate more quickly. The research in this paper can quickly diagnose the automation technology substation primary equipment and fault phenomena, and its establishment of the whole process is easy, the scope of application is relatively high, and it has wide applicability.
Research on the cultivation mechanism of “double-creative” talents in ethnic regions with the integration of industry and education in the context of the Internet

Abstract In the context of the rapid development of the Internet and big data technology, it is both an opportunity and a challenge for cultivating “dual-innovation” talents in ethnic regions. This paper proposes a fuzzy hierarchical analysis-fuzzy comprehensive evaluation model to study the cultivation mechanism of “dual-creative” talents in ethnic regions. The fuzzy hierarchical analysis-fuzzy comprehensive evaluation model consists of the evaluation system, a fuzzy comprehensive evaluation method, and a fuzzy hierarchical analysis. Then, based on the characteristics of fuzzy hierarchical analysis, the factors are organized and stratified to obtain a reasonable index set and build a hierarchical analysis structure. Using the fuzzy comprehensive evaluation method to analyze the school-enterprise cooperation in the integration of industry education in Tibetan higher vocational colleges, the results show that 55.4% of the students chose extremely unsatisfactory and unsatisfactory, indicating that they are not satisfied with the current communication status between the school and the internship enterprises, and another 28.6% of the students chose uncertainty full or unsatisfactory. This study explores the causes of the problems in the integration of industry-education personnel training from four levels: government, enterprises, society, and schools, and proposes guiding reference values for the integration of industry-education personnel training in Tibetan higher education institutions.
Monitoring System for Temperature and Humidity Sensors in the Production Room Using Node-Red as the Backend and Grafana as the Frontend

The client from the TRIAS project is facing issues with a decrease in production quality or defective products. They require a monitoring system for temperature and humidity in the production area, providing real-time notifications in case of any anomalies in temperature and humidity. However, the project has a limited budget, which poses a challenge for the contractor in developing a monitoring system that tracks temperature and humidity changes using temperature and humidity sensors as the data source. It should also provide alarms if the temperature and humidity values exceed the standard values for the room. Additionally, the pricing offer should be adjusted using information technology. The research methodology used in this study includes qualitative methods such as observation, literature review, and interviews to gather data on the mentioned issues. The SWOT method is used to analyze business process problems, while the Waterfall method is employed for system development. Based on the research findings, the researcher concludes that this project requires cost reduction in material usage and also needs a data visualization application for the mentioned sensors. The visualization application system utilizes Grafana as the frontend, chosen for its high flexibility in processing. The temperature and humidity data obtained from the sensors will be recorded by Node-Red as the backend and synchronized on the server. The data stored on the server will be saved in a MySQL database. The data from the database will be synchronized with Grafana for processing and visualization, presenting the data in easily understandable graphical forms.
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks

Combinatorial optimization finds an optimal solution within a discrete set of variables and constraints. The field has seen tremendous progress both in research and industry. With the success of deep learning in the past decade, a recent trend in combinatorial optimization has been to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning (ML) models. In this paper, we investigate two essential aspects of machine learning algorithms for combinatorial optimization: temporal characteristics and attention. We argue that for the task of variable selection in the branch-and-bound (B&B) algorithm, incorporating the temporal information as well as the bipartite graph attention improves the solver’s performance. We support our claims with intuitions and numerical results over several standard datasets used in the literature and competitions. (Code is available at: https://developer.huaweicloud.com/develop/aigallery/notebook/detail?id=047c6cf2-8463-40d7-b92f-7b2ca998e935 .)
Overcoming the Limitations of Localization Uncertainty: Efficient and Exact Non-linear Post-processing and Calibration

Robustly and accurately localizing objects in real-world environments can be challenging due to noisy data, hardware limitations, and the inherent randomness of physical systems. To account for these factors, existing works estimate the aleatoric uncertainty of object detectors by modeling their localization output as a Gaussian distribution $$\mathcal {N}(\mu ,\,\sigma ^{2})\,$$ , and training with loss attenuation. We identify three aspects that are unaddressed in the state of the art, but warrant further exploration: (1) the efficient and mathematically sound propagation of $$\mathcal {N}(\mu ,\,\sigma ^{2})\,$$ through non-linear post-processing, (2) the calibration of the predicted uncertainty, and (3) its interpretation. We overcome these limitations by: (1) implementing loss attenuation in EfficientDet, and proposing two deterministic methods for the exact and fast propagation of the output distribution, (2) demonstrating on the KITTI and BDD100K datasets that the predicted uncertainty is miscalibrated, and adapting two calibration methods to the localization task, and (3) investigating the correlation between aleatoric uncertainty and task-relevant error sources. Our contributions are: (1) up to five times faster propagation while increasing localization performance by up to 1%, (2) up to fifteen times smaller expected calibration error, and (3) the predicted uncertainty is found to correlate with occlusion, object distance, detection accuracy, and image quality.
Long-Tail Augmented Graph Contrastive Learning for Recommendation

Graph Convolutional Networks (GCNs) has demonstrated promising results for recommender systems, as they can effectively leverage high-order relationship. However, these methods usually encounter data sparsity issue in real-world scenarios. To address this issue, GCN-based recommendation methods employ contrastive learning to introduce self-supervised signals. Despite their effectiveness, these methods lack consideration of the significant degree disparity between head and tail nodes. This can lead to non-uniform representation distribution, which is a crucial factor for the performance of contrastive learning methods. To tackle the above issue, we propose a novel Long-tail Augmented Graph Contrastive Learning (LAGCL) method for recommendation. Specifically, we introduce a learnable long-tail augmentation approach to enhance tail nodes by supplementing predicted neighbor information, and generate contrastive views based on the resulting augmented graph. To make the data augmentation schema learnable, we design an auto drop module to generate pseudo-tail nodes from head nodes and a knowledge transfer module to reconstruct the head nodes from pseudo-tail nodes. Additionally, we employ generative adversarial networks to ensure that the distribution of the generated tail/head nodes matches that of the original tail/head nodes. Extensive experiments conducted on three benchmark datasets demonstrate the significant improvement in performance of our model over the state-of-the-arts. Further analyses demonstrate the uniformity of learned representations and the superiority of LAGCL on long-tail performance.
Learning to Play Text-Based Adventure Games with Maximum Entropy Reinforcement Learning

Text-based adventure games are a popular testbed for language based reinforcement learning (RL). In previous work, deep Q-learning is most often used as the learning agent. Q-learning algorithms are difficult to apply to complex real-world domains, for example due to their instability in training. Therefore, we adapt the Soft-Actor-Critic (SAC) algorithm to the domain of text-based adventure games in this paper. To deal with sparse extrinsic rewards from the environment, we combine the SAC with a potential-based reward shaping technique to provide more informative (dense) reward signals to the RL agent. The SAC method achieves higher scores than the Q-learning methods on many games with only half the number of training steps. Additionally, the reward shaping technique helps the agent to learn the policy faster and improve the score for some games. Overall, our findings show that the SAC algorithm is a well-suited approach for text-based games.
An overview on employee innovation behavior

Purpose: This article presents a review of recent research on factors that influence employee innovation behavior at the workplace. From the review of existing studies, this paper defines the concept of employee innovation behavior, explaining why these behaviors are so important for organizations and discusses the determinants of innovation behavior. Design/Methodology, based on a literature search on 15 peer-reviewed journals published during the period 2014-2021 and other relevant materials, it summarizes and discusses individual characteristics, employee competency and organizational level factors that have been found to influence innovativeness in organizations. Conclusion: Innovation has been recognized as one of the main strategies for organizations to remain competitive in today’s competitive economy. Employees’ innovation behavior is the key driver for organizational-wide innovation. Innovation behavior is the production of usable products, processes, or services originating from identifying problems to generating ideas. From the review of existing studies, this paper defines the concept of employee innovation behavior, explaining why these behaviors are so important for organizations and discusses the determinants of innovation behavior.