2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)最新文献_第8页

Research on Parallel Data Mining Based on Spark 基于Spark的并行数据挖掘研究

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00033

Jiali Shen

引用次数: 0

Optimization System of University Public Sports Training Data Management Based on Intelligent Optimization Algorithm 基于智能优化算法的高校公共体育训练数据管理优化系统

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00141

Guyu Dong, Jie Yu

{"title":"Optimization System of University Public Sports Training Data Management Based on Intelligent Optimization Algorithm","authors":"Guyu Dong, Jie Yu","doi":"10.1109/ISAIEE57420.2022.00141","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00141","url":null,"abstract":"Since many colleges and universities have always used the traditional manual filling and paper saving method to manage various information materials of training data, this brings many problems to daily management, such as data loss and difficulty in data. Find and work inefficient. In order to solve these problems, this paper intends to establish a management system for public sports training data, and hand over the complicated data management work to the system, which can not only reduce the workload of data management personnel, but also improve management efficiency and avoid data data loss, etc. In the design process, this paper uses the PAX page layout to design the system structure, and then introduces the particle swarm optimization algorithm in the intelligent optimization algorithm to optimize the performance of the system and make the data processing function of the system more perfect. This paper mainly aims at students' training performance data management, constructs training performance data table, and analyzes the realization process of training training performance data management. It is hoped that the promotion of this system to the management of public sports training data in colleges and universities can effectively improve the management efficiency.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114666635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Analysis of SECDED in Data Storage Transfer and Algorithm Optimization 数据存储传输中的SECDED分析及算法优化

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00018

Yuhang Hu

{"title":"The Analysis of SECDED in Data Storage Transfer and Algorithm Optimization","authors":"Yuhang Hu","doi":"10.1109/ISAIEE57420.2022.00018","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00018","url":null,"abstract":"The SECDED can correct up to 2-bit errors and detect up to 3-bit errors. To further improve the algorithm, this paper proposes solutions. For example, Using High- Dimensional Sphere Packing to increase the rate of our encoding method. The problem is that Assuming the data with an M, how can set the parity bit length of K meet the requirements of correcting a mistake? K checksum bits can have a value. One of these values indicates the data is accurate. The remaining 1-value means that the errors in the data can meet: $-1 > boldsymbol{m}+boldsymbol{K} (>boldsymbol{M}+boldsymbol{K}$ for the total length of the encoding). In theory, a K check code can determine which one (including the information code problems and check code). In the future, decentralized network architecture and native artificial intelligence (AI) capability are two significant trends of 6G networks. The existing centralized AI models that rely on cloud servers or terminals will be challenging to sustain the distributed intelligent cooperation requirements of multi- terminals and multi-nodes in 6G networks. Data collection and processing, AI in model training, model deployment, and reasoning get some new challenges through this new decentralized network environment. Aiming at the characteristics of heterogeneous mass terminal equipment, the significant difference in computing capacity, and dynamic change of communication network conditions in the 6G network decentralized computing environment, this paper analyses the development trend of decentralized artificial intelligence and relevant technologies and theories. It puts forward relevant forward-looking technical challenges and research directions.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124430835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on the Construction of English Teaching Modules Based on Computer Intelligence 基于计算机智能的英语教学模块构建研究

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00059

Shaoling Xiong

引用次数: 0

Research on Reinforcement Learning algorithms in Computer Vision 计算机视觉中强化学习算法的研究

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00057

Jiahui Lu, Mingyue Qin, Yuning Tong

引用次数: 0

Research on Basketball Training Optimization System under Computer Big Data Technology 基于计算机大数据技术的篮球训练优化系统研究

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00058

Wei Jingwei, Li Yana

{"title":"Research on Basketball Training Optimization System under Computer Big Data Technology","authors":"Wei Jingwei, Li Yana","doi":"10.1109/ISAIEE57420.2022.00058","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00058","url":null,"abstract":"Based on LAN technology, this paper developed an intelligent monitoring system for basketball training posture. First, the wireless sensing network is used to acquire the image information of the basketball training pose, and then the acquired image information is fused in three-dimensional fashion with a gray outline marker to obtain a visual imaging model, and the correctness of the basketball training pose is determined based on the pixel differential feature and visual imaging results. By analyzing the image features and performing automatic aggregation matching,this method combines the motion information features in the image with the local edge information, and combines it with the edge information entropy and characteristic values of the three-dimensional pose to construct an intelligent monitoring output optimization model. This paper introduces the development and design of an intelligent monitoring system for basketball sports attitude based on intelligent embedded technology. The simulation results show that the human mobility stability and performance of the system are strong.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124723268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Teaching Quality Evaluation and Software Implementation Based on ID3 Decision Tree Algorithm 基于ID3决策树算法的教学质量评价及软件实现

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00076

Bi Yan, Song Danning

引用次数: 0

Self-attentive mechanism-based supervised comparative learning 基于自我注意机制的监督比较学习

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00048

Chaoxiang Si

{"title":"Self-attentive mechanism-based supervised comparative learning","authors":"Chaoxiang Si","doi":"10.1109/ISAIEE57420.2022.00048","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00048","url":null,"abstract":"To address the intra-class diversity and inter-class similarity issues in traditional contrast learning, this paper proposes a supervised contrast learning based on a self-attentive mechanism that can effectively increase the feature extraction ability. The proposed method consists of two stages: feature encoder pre-training and linear classifier fine-tuning. In the feature encoder pre-training phase, the supervised contrast loss exploits the labeling information of the data to minimize the distance between similar images in the embedding space and maximize features of different categories as far away as possible, enhancing the effect of contrast learning. Beyond that, the self-attentive mechanism-based block is introduced in the encoder module to explicitly build the interdependence between the convolutional feature channels and further improve the feature learning capability of the model. In the linear classifier fine-tuning stage, parameters of pre-trained encoder are fixed and only the classifier is fine tuned for the downstream classification task. Experiments on the CIFAR-10 and CIFAR-100 datasets demonstrate the superior of our proposed method.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"39 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121281128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Intelligent Test Method of Distributed Network Inbreak Based on Cluster Analysis 基于聚类分析的分布式网络入侵智能测试方法

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00109

Zhang Yunyun

{"title":"An Intelligent Test Method of Distributed Network Inbreak Based on Cluster Analysis","authors":"Zhang Yunyun","doi":"10.1109/ISAIEE57420.2022.00109","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00109","url":null,"abstract":"Due to the lack of large-scale processing of network data in the process of distributed network inbreak intelligent detection, the detection accuracy is low. Therefore, a distributed network inbreak intelligent test method based on cluster analysis is proposed. The distributed network data is preprocessed through data attribute feature transformation, data normalization, data standardization and data dimensionality reduction. According to the preprocessing results, the distributed network data collection is divided into multiple clusters using the fuzzy K-means clustering algorithm., compute the remove from each cluster centre to other data targets, use Euclidean remove to construct the goal function of partition quality, extract distributed network inbreak data, determine the cost function of the convolutional neural networks-gate recurrent unit network model, and use the stochastic gradient descent algorithm to The convolutional neural networks-gate recurrent unit network model is trained, and the extracted distributed network inbreak data is input as an initial sample into the trained convolutional neural networks-gate recurrent unit network model, the model is solved, and the distributed network inbreak intelligent detection results are output. The analysis of the experimental results shows that the proposed method has higher precision and better detection effect in the intelligent detection of distributed network inbreak.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121516871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Formative Evaluation System of Online and Offline Blended Teaching Based on Grey Fuzzy Theory 基于灰色模糊理论的线上线下混合式教学形成性评价体系

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00080

Benfeng Yu

引用次数: 0