Reviewerly/exHarmony huggingface dataset:
An Ameliorated Algorithm of Hybrid Vulture Optimization Based on Lens Reverse Learning Strategy

Cloud platform usually allows multiple tasks to perform in parallel, and as an important cornerstone of cloud platforms, the quality of task scheduling strategies is related to the replication quality and system performance of the entire cloud analysis platform. Its scheduling efficiency directly affects the operating factor of cloud computing virtual machines and other software and hardware resources and the quality of service for users. To improve the productiveness of the process of assigning tasks to virtual computing resources in cloud platforms, there advanced a mixed meta heuristic algorithm algorithm, called LBE algorithm (LiL-Baldeagle-ESDA Optimization Algothrim), which combines the reflection imaging principle of lens, finds another position point in the solution space, broaden the solution domain, and prevent the solution from plung in the global suboptimal or part optimal solution. Combining the advantages of the Electrostatic Discharge Algorithm (ESDA) to address the shortcomings of the Bald Eagle Search Optimization Algorithm (BES) in global solution domain. This article uses 23 classic checkout functions from early CEC to trail the behavior of algorithm, and contrast to the results of several scriptures' algorithms. The test value on the function set indicate that the proposed LBE has advantages within the solution space, convergence rate and optimization ability.
A Workshop Safety Inspection Model Based on Deep Learning

As an important branch of machine vision, video surveillance has been playing a vital role in traffic, security, monitoring and other fields. Especially driven by the development of deep learning and intelligent manufacturing, the monitoring field has become more and more intelligent and refined. However, some production scenarios still use traditional monitoring methods so far, relying too much on manual watch, and it is impossible to achieve real-time and effective alarm and feedback for abnormal behaviors on the production site. Based on this, this paper designs an intelligent security monitoring model for abnormal behaviors in the industrial scene based on deep learning technology, extracts effective features from the video and represents the target, detects violations or unsafe behaviors in the video in real time, and gives timely feedback to improve the intelligence of the industrial production management system and ensure production safety.
Knowledge Graph Completion Algorithm Based On Multimodal Representation Learning

In the existing multi-modal knowledge graph, the entity picture datasets have irrelevant pictures, resulting in noise of knowledge representation learning. Therefore, this paper proposes to use the image filtering algorithm to remove the entity irrelevant pictures in the picture datasets to improve the quality of datasets, and use the image enhancement algorithm to improve the number of pictures in the picture datasets. In this paper, the pretrained-model Bert was used to extract the text features, the pretrained-model CLIP was used to extract the image features, and the modal fusion algorithm was used to get the final representation of the triples. In addition, for textual and visual feature fusion, this paper proposes a multi-lavel residual network features fusion algorithm, and uses the public dataset FB15K-237 to test the proposed knowledge completion algorithm (MMKGR) based on multimodal knowledge representation learning. After comparing with other multimodal knowledge completion algorithms,as TransE, VisualBERT_base,IKRL and TransAE the algorithm proposed in this paper is superior to other algorithms in Hit@1, Hit@10 and MR.
Study on Application of VR Technology in Teaching Tourism Management Courses

With the development of VR technology and related technology, the application of this technology in the teaching of higher education courses is becoming more and more widespread. In the current environment, many higher education schools are constantly changing and innovating their teaching models and teaching concepts in the hope of providing students with better teaching resources and teaching experiences. Many courses in tourism management are highly practical. By using relevant virtual reality technology in tourism courses, it helps to create a multi-dimensional teaching environment, deepen students' learning experience and enhance their practical skills. This paper analyzes the current shortcomings and difficulties of the teaching methods of tourism in higher education schools, and analysis the positive significance of VR technology applied to the teaching of tourism courses for improving the above problems. This provides some useful ideas for the enrichment of teaching tools and the improvement of teaching quality in tourism courses.
A Meta-Analysis of GeoGebra's Influence on Students' Learning Effect

The Education Informatization 2.0 Action Plan clearly points out that to promote the integration of information technology and disciplines and make use of technological advantages to reform teaching, education software has become a hot topic of discipline reform. As a dynamic and highly interactive discipline software tool, GeoGebra has become increasingly popular in mathematics, physics, chemistry and other disciplines. A large number of empirical studies have explored the application effect of GeoGebra, but the impact of GeoGebra on students' learning effect has not yet formed a unified conclusion. The research adopts the meta analysis method to screen and analyze 21 empirical research literatures on GeoGebra related topics at home and abroad. The research results show that GeoGebra has a significant positive role in promoting students' learning effect, and the impact on students' learning effect is regulated by the experimental cycle. According to the research on the results of the analysis element, the author puts forward suggestions on the application of GeoGebra in teaching, with a view to proving the role of GeoGebra in improving students' learning effect and providing reference for the popularization and application research of GeoGebra.
Chinese Book Keyword Extraction and Generation Algorithm Integrating Title and Topic Attention

In the era of big data, the number and types of book resources are increasing exponentially. However, the existing attribute information of books has a relatively coarse granularity for book descriptions, which not only easily leads users to fall into “information loss” when searching for resources, but also greatly limits the circulation of book resources. In response to the above problems, a Chinese book keyword extraction and generation algorithm that integrates title and subject terms attention (TEG) is proposed, and experiments are carried out on a self-built Chinese book data set. The results show that the improved algorithm has a greater improvement in accuracy, recall and F1 value compared with other comparison algorithms.
Detection and Segmentation of Brain Tumor by Using Modified Watershed Algorithm and Thresholding to Reduce Over-Segmentation

Digital image processing plays a crucial part in the detection, observation, and diagnosis of a brain tumor. From MRI images, automatic detection of a brain tumors is perhaps the most difficult and critical task in present medical imaging research. This paper proposes a modified marker-based watershed technique to be utilized for segmentation of images; before that, some pre-processing techniques were utilized for noise removal; and then, for the final segmentation processes, the Otsu’s threshold technique is also applied to get better segmented and noise-free images with the marker-controlled watershed algorithm. Statistical measurements and graphical representations of the proposed method and their results are also explained in this paper. The proposed techniques and experiments are executed through the MATLAB 2021 software.
Pre-Service Teachers' Behavioral Intention for AI-Integrated Instruction: A Path Analysis of the Theory of Motivation-Opportunity-Ability (MOA)

The advancement of artificial intelligence has presented teachers with unprecedented changes in their teaching practice, necessitating teachers proactively adapt to AI and be prepared to conduct AI-integrated instruction. Therefore, based on MOA (motivation-opportunity-ability) theory, this study investigated pre-service teachers' self-reported intention for AI-integrated instruction. For the direct effect, we confirmed that both intrinsic and extrinsic motivation positively influenced preservice teachers' behavioral intention for AI-integrated instruction. Also, we found motivation (intrinsic and extrinsic motivation) mediated the relation of facilitating conditions and perceived self-efficacy. The proposed framework will offer an approach for educators to design effective teacher-training curriculums based on the related factors of the behavioral intention for AI-integrated instruction.
An Effective Path for Rural Teachers to Use Digital Education Resources in Chinese Underdeveloped Areas: A Study Based on Grounded Theory

Digital education resources (DERs) is an important part of the digital transformation of education. At the same time, DERs has potentially important functions in improving the quality of rural education and narrowing down the gap between rural and urban education. However, how DERs can be effectively applied in rural schools in underdeveloped areas is the key to the effectiveness of this potential function. This study took a rural school in a Chinese underdeveloped area as a case, and conducted in-depth interviews with the school principal and 8 teachers who frequently use DERs. The interview data was proceeded in NVivo11 and openly coded into five core categories as schools' development planning, teaching environment support, teachers' professional development, teachers' community atmosphere and effective application mode according to the Grounded Theory. Finally, an effective path for rural teachers to use DERs in teaching was obtained. The study found that the effective path for DERs usage of rural teachers in underdeveloped areas was as follows: 1) The principal leads the teachers to jointly explore the development direction that is suitable for the actual situation of the school and is recognized by the teachers, 2) Teachers actively explore and establish a positive professional learning atmosphere, and normalize the application of DERs in teaching, 3) Finally, teachers will be rewarded with more active learning and exploration based on excellent and visible teaching effects.
Design and Implementation of Fast Start and Recovery of CNC System Based on AM335x

Based on AM335x processor, this paper completes the fast start and recovery function of CNC system. This function is implemented in Uboot, using MTD technology to partition Nand Flash, configure serial port, LCD, USB and other pin clocks, and modify and add functions that check and update USB files. Finally, the Uboot transplanted on AM335x in this paper shortens the startup time of the CNC system, provides an LCD boot interface, and realizes the function of quickly restoring the CNC system from the U disk.
Design and Practice of Effective Teaching Interaction Activities Based on Intelligent Classroom Teaching Interaction Behavior Data

The continuous development of the concept of wisdom education has a profound impact on the cultivation of learners at each stage of education. In China, higher education has already entered the popularization, stage and the number of students is increasing. The quality of undergraduate curriculum teaching is an important guarantee for personnel training. Meanwhile, the construction of intelligent classroom in colleges and universities has achieved initial results, and the study on teaching effect of intelligent classroom has become an issue of concern. Based on the analysis of the interactive behavior of smart classroom teaching, this study constructed the interactive coding system of smart classroom teaching, analyzed the existing problems of unenabled technology, single interaction form and fragmented interaction subject of smart classroom teaching in our school, and designed and implemented interactive activities of smart classroom teaching in response to these problems. Relevant practice data were collected to analyze the effect of interactive activities from the dimensions of teaching effectiveness including teaching interaction diversification, students' satisfaction with teaching interaction and students' ability development.
Using Fuzzy Clustering with SVM in Credit Classification

It suffers from the lack of user classification information in the credit data of the People's Bank of China, and there are a lot of descriptive variables for each user, which restricts the availability of credit reports in financial institutions. In order to address this issue, this paper marks the credit report by constructing fuzzy numbers and using fuzzy clustering, and classifies the credit users with support vector machines on the basis of the marks. From the experiments, it can be seen that fuzzy clustering gives an significant improvement in classifying users' credit information.
H-CE: An Event Correlation Discriminant Model Based on Event Composition Features and Embedded Representation

Finding a relationship between events and mining particular event types between events are the two objectives of the event correlation identification work. The approach put out in this research is capable of processing and analyzing the structured text representation content of events, such as control and monitoring on the Internet of Things, and it can also carry out Event Correlation Analysis in Collaborative Processes in Industrialization Scenarios. Previous studies mostly dealt with unstructured text data and employed techniques like event extraction, relation extraction, and deep learning models for correlation analysis. Since the data in this study is structured and categorized by different event kinds, it is challenging to utilize a deep learning model to directly analyze the relationships between events. Therefore, this paper first constructs a data set of character behavior characteristics (GKD-BehaviorData) and proposes a general event representation method. Then, based on the previous research on case reasoning and machine learning, a hybrid computing method (Hybrid-Composition&Embedding Method, H-CE) based on event composition features and knowledge embedding is proposed. The results of the studies show that this method is noticeably superior to the event correlation calculation method based on event correlation characteristics, with improvements in the acc and F1 values of 17.4% and 19.7%, respectively.
The Development and Practice of Artificial Intelligence General Courses Based on Task-driven in Secondary Vocational Schools

With the fast development of artificial intelligence technology, the popularization of artificial intelligence education is one of the significant contents of China's education development strategy. Therefore, vocational schools should take their responsibilities to meet the society's demands for “ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{AI}+\mathbf{X}$</tex> ” compound talents. The research analyzes the problems existing in the current artificial intelligence general courses in secondary vocational schools. For example, the insufficient research on teaching methodology, and the existing curriculum doesn't match the learning foundation of secondary vocational students. Therefore, combining the characteristics of artificial intelligence majors and the learning conditions of secondary vocational students, we carry out the practical explorations based on taskdriven teaching methods. Taking students as the main study body with tasks as the driving force, this paper mainly discusses the development and practice of artificial intelligence general courses in secondary vocational schools from the aspects of clarifying the main tasks of the curriculum, constructing the curriculum framework structure, clarifying the teaching design path and displaying curriculum cases, which provide theoretical reference and practical guidance for the general education course of artificial intelligence in secondary vocational schools.
Fatigue Monitoring and Awakening System Based on Eye Electrical and Head Movement Parameters Monitoring

When the human body is in a state of fatigue, its characteristics of eye electrical signals and head posture signals will change significantly. Based on the analysis and research of these two kinds of signals, a wearable EOG and head posture signal fatigue monitoring and wake-up system is proposed by combining music wake-up method. The single lead electrode is used to collect the EOG signal, and the three-axis gyroscope sensor is used to collect the acceleration and angular velocity signals when the human head moves. The characteristics of the blinking signal are analyzed through the EOG signal, and four head posture characteristics are analyzed according to the characteristics of the acceleration and angular velocity in the time domain. Finally, the BP neural network is used to identify the features of the relevant signals. When the fatigue features reach a certain level, physical stimulation, such as music, can help people wake up. The experimental results show that the EOG signal and acceleration signal can accurately analyze the fatigue characteristics and changes of testers, and the wake-up method is effective.
RDF Date Storage Scheme Based on Graph Database

The structure can be directly mapped to the RDF model, and RDF data can be stored in the graph database. There is no need to rebuild the RDF graph to query the semantic information data of RDF. Based on the RDF data storage Scheme of graph database, the research status of RDF data storage and the transformation of SPARQL query language into Cypher graph query language are discussed emphatically. It is verified by experiments that the Neo4j graph database can further improve the query efficiency while satisfying the query of RDF graph data.
A Sensitive Image Generation Method Based on Improved PatchGAN

Aiming at the problem of less data and unbalanced number of sensitive image samples in the deep learning task of sensitive image detection, this paper proposes a sensitive image generation method SI-GAN based on improved PatchGAN. The SI-GAN generator uses the DUD module, and uses the concat method to increase the number of channels of the feature map in the quantitative dimension, which improves the generation ability of the generator. The SI-GAN discriminator uses the RBL module to improve PatchGAN, extract more advanced feature information, and increase the discriminant's discriminative ability. The experimental results show that SI-GAN has better results in terms of generated image quality. The FID value of SI-GAN is 5.87 lower than that of PatchGAN. The IS value of SI-GAN is 5.09 more than PatchGAN. The SI-GAN proposed in this paper is able to optimize the quality of the generated images and generate images with more details.
Mediating Effect of Mobile Motivation on the Correlation between Pre-achievement and Post-achievement of Chinese as a Second Language

Due to the affordances of m-learning and mobile-assisted language learning (MALL), ChineseSkill, a mobile app, is used in this study to enhance the learning effects of Chinese as a Second Language (CSL). This study aims to investigate the mediating effect of mobile motivation on the correlation between CSL learners' pre-achievement and post-achievement. The respondents were CSL beginners selected at a private university college in Melaka, Malaysia, using the purposive sampling method. There were 95 valid cases in total. Descriptive analysis revealed that the respondents exhibited moderate levels of mobile motivation. Besides, learners made an obvious improvement from pre-achievement to post-achievement. The results from PLS-SEM show that the correlation between pre-achievement and post-achievement, the correlation between pre-achievement and mobile motivation, and the correlation between mobile motivation and post-achievement were all positive and significant. Moreover, mobile motivation was a partial mediator for the correlation between pre-achievement and post-achievement. Therefore, mobile apps such as ChineseSkill should be used to improve learners' mobile motivation and achievement. Besides, future studies should pay more attention to the role of mobile motivation and other potential mediators and moderators. Moreover, various types of data collection methods should be used in future studies.
Research on Power Dispatching Speech Recognition Based on Improved Transformer

Now, speech recognition [1] has developed rapidly. Transformer network model architecture is constructed by encoder and decoder. has become popular and applied to speech recognition. The Transformer model has achieved very good results in ordinary daily scenes. In the production environment of power dispatching, speech recognition requires low error rate and faster recognition speed. Therefore, some improvements have been made to the Transformer model. Then, the core attention mechanism in Transformer is mainly introduced. According to the characteristics of voice dataset in power dispatching, the model is improved and the attention mechanism is lightweight. Finally, through experimental training, we use the improved model to train on the power dispatching voice dataset, and then evaluate the trained model.
Real-Time Classification and Detection of Garbage Based on Improved Yolov5 and Embedded System

Garbage classification is an important environmental issue, and accurate sorting of different types of waste is critical for effective recycling and waste management. Deep learning technology has great potential for garbage classification, and with the growing importance of waste management, it is highly likely that the role of image recognition in this field will continue to increase in the future. However, most of the existing garbage classification and detection methods only consider the detection accuracy of garbage, which are not real-time and can hardly meet the practical application of garbage classification. To address this problem, a new real-time classification and detection of garbage based on improved yolov5 and adapted to an embedded system from UAV images is proposed, which is named as G-YOLOv5s. First, residual dilated convolution module(Res-DConv) is employed to extract the spatial features, which can increase the receptive field. Then, a feature fusion module(BDSCAM) is designed to enhance the expressive ability of object feature, which could improve the classification performance of detector. The localization task is performed using a Double-Head method, which is an integration system of fully connected and convolutional heads for bounding box regression and classification. The proposed G-YOLOv5s method was evaluated using Huawei Garbage Classification Challenge Cup dataset. We found that the experimental results demonstrate that the proposed method is high accuracy and efficient for garbage classification and detection on the embedded system. This approach is robust and suitable for practical application in garbage classification scenarios.