Proceedings of the 6th International Conference on Computer Science and Application Engineering最新文献_第6页

Design and Implementation of Hainan Meteorological Information Service Website Based on WebGIS 基于WebGIS的海南省气象信息服务网站的设计与实现

Proceedings of the 6th International Conference on Computer Science and Application Engineering Pub Date : 2022-10-21 DOI: 10.1145/3565387.3565445

Jianhua Du, Jinfeng Li, Lijun Wang, Jichao Liu

{"title":"Design and Implementation of Hainan Meteorological Information Service Website Based on WebGIS","authors":"Jianhua Du, Jinfeng Li, Lijun Wang, Jichao Liu","doi":"10.1145/3565387.3565445","DOIUrl":"https://doi.org/10.1145/3565387.3565445","url":null,"abstract":"The user-oriented meteorological information services are becoming an inevitable trend, which calls for providing such services by overlaying geographic locations and meteorological information. Thus, this paper analyzes the Web geographic information system (WebGIS) technology and the J2EE application framework Struts + Spring + Hibernate (SSH). Moreover, combined with the meteorological metadata attributes and spatial location characteristics, we illustrate the basic meteorological data flow of China's integrated meteorological information service system (CIMISS). Furthermore, under the Model + View + Controller (MVC) and meteorological data utilizing GIS, we design the framework and functions of the Hainan Meteorological Information Service Website, which integrates forecasting and early warning, meteorological monitoring, and decision-making services. The results of several functional and performance tests reveal that the website's operations present high availability and reliability and provide the public with timely and effective basic meteorological data service products.","PeriodicalId":182491,"journal":{"name":"Proceedings of the 6th International Conference on Computer Science and Application Engineering","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124077769","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

Implementation of Cloud-based Urban Rail Big Data Platform 基于云的城市轨道大数据平台实施

Proceedings of the 6th International Conference on Computer Science and Application Engineering Pub Date : 2022-10-21 DOI: 10.1145/3565387.3565402

Lili Chen, Ying-chen Di, Lele Zhang

{"title":"Implementation of Cloud-based Urban Rail Big Data Platform","authors":"Lili Chen, Ying-chen Di, Lele Zhang","doi":"10.1145/3565387.3565402","DOIUrl":"https://doi.org/10.1145/3565387.3565402","url":null,"abstract":"Facing the wave of digital transformation of urban rail, it has proposed a cloud-based big data platform construction plan following the innovative urban rail development in the paper. The cloud platform provides virtualized resources and services such as computing, storage, networking, security, and Platform-as-a-Service components for the big data platform. The big data platform works as the Data-as-a-Service layer of the cloud, which integrates multiple business application data, conducts data governance, forms data assets, and provides secure, universal, and flexible data sharing services to various advanced intelligent applications. The low-code historical data interface and real-time data service is an innovative function to meet new demands for intelligent operation. Data modeling and optimization in the face of the characteristics of the Hadoop platform are also mentioned. The solution provides data support for the application of intelligent urban rail and realizes the integration of cloud and big data platforms. Meanwhile, the data is passed from producers to consumers through data reorganization of big data platforms, which could isolate the influence of data errors and model changes.","PeriodicalId":182491,"journal":{"name":"Proceedings of the 6th International Conference on Computer Science and Application Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125073217","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

Scientific Knowledge Graph-driven Research Profiling 科学知识图谱驱动的研究分析

Proceedings of the 6th International Conference on Computer Science and Application Engineering Pub Date : 2022-10-21 DOI: 10.1145/3565387.3565423

Jiao Li, Tan Sun, Guojian Xian, Yongwen Huang, Ruixue Zhao

引用次数: 0

Complementary Random Walk: A New Perspective on Graph Embedding 互补随机漫步:图嵌入的新视角

Proceedings of the 6th International Conference on Computer Science and Application Engineering Pub Date : 2022-10-21 DOI: 10.1145/3565387.3565396

Yang Chen, Chunyan Xu, Tong Zhang, Guangyu Li

{"title":"Complementary Random Walk: A New Perspective on Graph Embedding","authors":"Yang Chen, Chunyan Xu, Tong Zhang, Guangyu Li","doi":"10.1145/3565387.3565396","DOIUrl":"https://doi.org/10.1145/3565387.3565396","url":null,"abstract":"Random-walk based graph embedding algorithms like DeepWalk and Node2Vec are widely used to learn distinguishable representations of the nodes in a network. These methods treat different walks starting from every node as sentences in language to learn latent representations. However, nodes in a unique walking sequence often appear repeatedly. This situation results in the latent representations obtained by the aforementioned algorithms cannot capture the relationship between unconnected nodes, which have similar node features and graph topology structures. In this paper, we propose Complementary Random Walk (CRW) to solve this problem and embed the nodes in a network to obtain more robust low-dimensional vectors. By conducting a K-means clustering algorithm to cluster different features extracted from the graph, we can supply the original random walk with many other walking sequences, which consist of different unconnected nodes. And those nodes are sampled from the same cluster based on graph features, such as node degree, motif features, and so on. Our experiments achieve comparable or superior performance compared with other methods, validating the effectiveness of CRW.","PeriodicalId":182491,"journal":{"name":"Proceedings of the 6th International Conference on Computer Science and Application Engineering","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133758103","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 Network Configuration Verification Based on Association Analysis 基于关联分析的网络配置验证研究

Proceedings of the 6th International Conference on Computer Science and Application Engineering Pub Date : 2022-10-21 DOI: 10.1145/3565387.3565404

Xiaoming He, Shao-wen Li, Zijing He, Xing Peng

引用次数: 0

Design of Smart Lamp Pole System Based on All-Optical Pol Architecture 基于全光Pol结构的智能灯杆系统设计

Proceedings of the 6th International Conference on Computer Science and Application Engineering Pub Date : 2022-10-21 DOI: 10.1145/3565387.3565439

Shuang Liu, Wen-Hui Wu, Jingyi Wang

引用次数: 0

A Multi-information Embedding Link Prediction Approach with Collective Attention Flow Network 基于集体注意流网络的多信息嵌入链接预测方法

Proceedings of the 6th International Conference on Computer Science and Application Engineering Pub Date : 2022-10-21 DOI: 10.1145/3565387.3565398

Lujuan Jiang, Yong Li, Manfu Ma, Yanjun Fan, Yiding Zhang

{"title":"A Multi-information Embedding Link Prediction Approach with Collective Attention Flow Network","authors":"Lujuan Jiang, Yong Li, Manfu Ma, Yanjun Fan, Yiding Zhang","doi":"10.1145/3565387.3565398","DOIUrl":"https://doi.org/10.1145/3565387.3565398","url":null,"abstract":"Link prediction and community detection reveal the basic mechanism and evolution law of the network from different perspectives, and the relationship between community members can provide valuable information for link prediction. Most link prediction methods are based on local structural features and lack the application of community topology information. To deal with this challenge, we propose a link prediction method NCELP with collective attention flow network. Firstly, we apply the Louvain algorithm to give each node a community label as an explicit feature. Secondly, using local structural features and community topology information, learned node and community embedding, which serve as implicit features. Finally, combined with the graph structure features, the link prediction problem is transformed into a binary classification problem and realized by the existence probability of the edge. We validated NCELP using behavior data from China Internet Network Information Center with more than 30,000 online users and three public datasets. Experimental results verify that NCELP not only outperforms the state-of-the-art methods on real-world datasets but also improves its AUC value by at least 9.83% and its AP value by at least 3.39%.","PeriodicalId":182491,"journal":{"name":"Proceedings of the 6th International Conference on Computer Science and Application Engineering","volume":"887 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116170394","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

Tomato Disease Identification Application Based on EfficientNetV2 基于EfficientNetV2的番茄病害识别应用

Proceedings of the 6th International Conference on Computer Science and Application Engineering Pub Date : 2022-10-21 DOI: 10.1145/3565387.3565399

Zhanhao Shi, Cui Wang, Lin Zhao

引用次数: 1

Research on Attentional EEG Based on Granger Causality of Polynomial Kernel 基于多项式核格兰杰因果关系的注意脑电图研究

Proceedings of the 6th International Conference on Computer Science and Application Engineering Pub Date : 2022-10-21 DOI: 10.1145/3565387.3565419

Jun Wang

{"title":"Research on Attentional EEG Based on Granger Causality of Polynomial Kernel","authors":"Jun Wang","doi":"10.1145/3565387.3565419","DOIUrl":"https://doi.org/10.1145/3565387.3565419","url":null,"abstract":"Attention is a neurocognitive process, which specifically describes a person's mental and physical ability to concentrate on certain things, covering a series of mental activities such as perception, analysis, logic, reasoning, and imagination. It's hard to concentrate for attention deficit patients with symptoms of impulsive and restless, which led to a decline in learning and work efficiency and even seriously affect our normal life. Therefore, it is necessary for us to study attentional Electroencephalogrphy (EEG), which has a certain guiding role in solving clinical attention-related diseases. In this paper, the Granger Causal algorithm based on polynomial kernel function is used to study the directionality of the interaction between the left-brain and right-brain EEG signals. The experimental results show that when m and p are fixed (m represents the embedding dimension, p represents the degree of the highest term of the polynomial kernel function), in the counting state, the Granger Causality Index from left brain to right brain is significantly larger than that from right brain to left brain. In the closed-eye state and idle state, the Granger Causality Index from left brain to right brain significantly smaller than in the opposite direction. It is further explained that in the counting state, the influence of the EEG signal of the left brain on the EEG signal of the right brain is greater than the influence of the right brain on the left brain. In the closed-eye state and idle state, the causal effect of the left brain on the right brain is less than that of the right brain on the left brain. In addition, we also compared the Granger Causality Index of the same individual in the three states. We found that the Granger Causality Index in the counting state is larger than the other two states, and it is the smallest in the closed-eye state.","PeriodicalId":182491,"journal":{"name":"Proceedings of the 6th International Conference on Computer Science and Application Engineering","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121117969","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 Sleep EEG Signals Based on IOTA 基于IOTA的睡眠脑电信号研究

Proceedings of the 6th International Conference on Computer Science and Application Engineering Pub Date : 2022-10-21 DOI: 10.1145/3565387.3565390

Jun Wang

{"title":"Research on Sleep EEG Signals Based on IOTA","authors":"Jun Wang","doi":"10.1145/3565387.3565390","DOIUrl":"https://doi.org/10.1145/3565387.3565390","url":null,"abstract":"As a nonlinear analysis method based on permutation, internal composition alignment (IOTA) algorithm can study the coupling between systems by calculating the coupling coefficient between two time series. In this paper, the internal composition alignment (IOTA) algorithm is used to study the sleep EEG signals generated by the human body in different sleep periods. Firstly, the IOTA coefficients between different time series calculated by this method are used as nodes to construct the sleep function networks in different sleep periods, and the statistical characteristics of networks such as node degree and clustering coefficient are selected to compare different sleep networks. The results show that the IOTA coefficient and the node average degree and aggregation coefficient of EEG network in NREM-I period are higher than those in awake period, indicating that the complexity of EEG network in NREM-I period is higher than that in awake period, and that the coupling degree in NREM-I period is also higher than that in awake period. This experiment proves the effectiveness of IOTA algorithm for analyzing sleep function network. This algorithm can be used to study sleep EEG staging. At the same time, it also provides an important reference for the research, clinical diagnosis and treatment of sleep diseases in the future.","PeriodicalId":182491,"journal":{"name":"Proceedings of the 6th International Conference on Computer Science and Application Engineering","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128191952","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