Yating Huang, Chunhai Li, Mingfeng Chen, Zhaoyu Su
When Kafka is used in production environments, a large number of parameters are provided to facilitate user configuration for specific application environments in order to obtain better performance. However, configuring Kafka's parameters requires in-depth knowledge of the user, which is far beyond the ability of the average user and prevents Kafka from obtaining better performance. To address this problem, we propose an ACT-SAGAN method that adds a self-attention mechanism to the generative adversarial network model to capture the associations between hidden structures in good configuration combinations and configuration parameters, which uses these hidden structures and associations to generate better configuration combinations to improve Kafka's performance. Experimental results show that the algorithm improves Kafka's throughput and reduces latency after deployment for the configuration combinations generated by Kafka.
{"title":"ACT-SAGAN: Automatic Configuration Tuning for Kafka with Self-Attention Generative Adversarial Networks","authors":"Yating Huang, Chunhai Li, Mingfeng Chen, Zhaoyu Su","doi":"10.1145/3569966.3570024","DOIUrl":"https://doi.org/10.1145/3569966.3570024","url":null,"abstract":"When Kafka is used in production environments, a large number of parameters are provided to facilitate user configuration for specific application environments in order to obtain better performance. However, configuring Kafka's parameters requires in-depth knowledge of the user, which is far beyond the ability of the average user and prevents Kafka from obtaining better performance. To address this problem, we propose an ACT-SAGAN method that adds a self-attention mechanism to the generative adversarial network model to capture the associations between hidden structures in good configuration combinations and configuration parameters, which uses these hidden structures and associations to generate better configuration combinations to improve Kafka's performance. Experimental results show that the algorithm improves Kafka's throughput and reduces latency after deployment for the configuration combinations generated by Kafka.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"1 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":"130392337","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}
For the military manufacturing field, through a detailed analysis of ERP business processes and finance, a business-financial integration scheme is proposed, which opens the data link between ERP and business finance platform by establishing synchronous update of business and financial information data, and realizes the financial accounting of manufacturing business from sales to delivery, procurement to payment, demand to manufacturing, etc., which includes scientific research and trial production and mass production. The integrated integration improves the efficiency of accounting and the accuracy of data.
{"title":"Research and application of business finance integration solution for military manufacturing","authors":"Lu Jia, Pu Zhao, Qian Li","doi":"10.1145/3569966.3570042","DOIUrl":"https://doi.org/10.1145/3569966.3570042","url":null,"abstract":"For the military manufacturing field, through a detailed analysis of ERP business processes and finance, a business-financial integration scheme is proposed, which opens the data link between ERP and business finance platform by establishing synchronous update of business and financial information data, and realizes the financial accounting of manufacturing business from sales to delivery, procurement to payment, demand to manufacturing, etc., which includes scientific research and trial production and mass production. The integrated integration improves the efficiency of accounting and the accuracy of data.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"8 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":"127779075","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}
Abstract—The severe acute respiratory syndrome coronavirus 2 is a novel type of coronavirus that causes COVID-19. The COVID-19 virus has recently infected more than 590 million individuals, resulting in a global pandemic. Traditional diagnosis methods are no longer effective due to the exponential rise in infection rates. Quick and accurate COVID-19 diagnosis is made possible by machine learning (ML), which also assuages the burden on healthcare systems. After the effective utilization of Cough Audio Signal Classification in diagnosing a number of respiratory illnesses, there has been significant interest in using ML to enable universal COVID-19 screening. The purpose of the current study is to determine people's COVID-19 status through machine learning algorithms. We have developed a Random Forest based model and achieved an accuracy of 0.873 on the COUGHVID dataset, demonstrates the potential of using audio signals as a cheap, accessible, and accurate COVID-19 screening tool.
{"title":"\"SOS Signal\" in Breathing Sound - Rapid COVID-19 Diagnosis Based on Machine Learning","authors":"Hanxiang Wang","doi":"10.1145/3569966.3570100","DOIUrl":"https://doi.org/10.1145/3569966.3570100","url":null,"abstract":"Abstract—The severe acute respiratory syndrome coronavirus 2 is a novel type of coronavirus that causes COVID-19. The COVID-19 virus has recently infected more than 590 million individuals, resulting in a global pandemic. Traditional diagnosis methods are no longer effective due to the exponential rise in infection rates. Quick and accurate COVID-19 diagnosis is made possible by machine learning (ML), which also assuages the burden on healthcare systems. After the effective utilization of Cough Audio Signal Classification in diagnosing a number of respiratory illnesses, there has been significant interest in using ML to enable universal COVID-19 screening. The purpose of the current study is to determine people's COVID-19 status through machine learning algorithms. We have developed a Random Forest based model and achieved an accuracy of 0.873 on the COUGHVID dataset, demonstrates the potential of using audio signals as a cheap, accessible, and accurate COVID-19 screening tool.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"1 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":"128971046","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}
Wei Guo, Zhongjun Fu, Jiang Sun, Lei Wang, Jing Zhang
Amazon Mechanical Turk (Mturk), as the world's largest micro-task crowdsourcing platform, provides task search services to thousands of users every day. However, the lack of navigation panel in the platform's existing user interface makes task searching difficult and tedious, resulting in users rarely finding acceptable tasks among the countless crowdsourced assignments. To lower the task search threshold of Mturk, this study creates an automated task navigation structure by combining the unsupervised task clustering algorithm and the topic recognition algorithm, which can be fine-tuned according to task characteristics and help users focus on the task type quickly and precisely. Since the navigation panel is a non-existent element of Mturk, this paper develops a well-designed questionnaire to investigate users' perceptions of the improved interface. The results show that users were dissatisfied with Mturk's current UI, preferring the navigation interface outlined in this study. In conclusion, this work provides theoretical guidance for building similar automated task navigation panels for other crowdsourcing platforms while addressing the practical problems of Mturk.
Amazon Mechanical Turk (Mturk)作为全球最大的微任务众包平台，每天为成千上万的用户提供任务搜索服务。然而，平台现有的用户界面缺少导航面板，使得任务搜索困难且繁琐，导致用户在无数的众包任务中很少能找到可接受的任务。为了降低Mturk的任务搜索阈值，本研究将无监督任务聚类算法与主题识别算法相结合，创建了一个自动化的任务导航结构，该结构可以根据任务特征进行微调，帮助用户快速准确地关注任务类型。由于导航面板是Mturk不存在的元素，本文开发了一个精心设计的问卷调查，以调查用户对改进界面的看法。结果表明，用户对Mturk目前的UI不满意，更喜欢本研究中概述的导航界面。综上所述，本文在解决Mturk的实际问题的同时，为其他众包平台构建类似的自动化任务导航面板提供了理论指导。
{"title":"Task navigation panel for Amazon Mechanical Turk","authors":"Wei Guo, Zhongjun Fu, Jiang Sun, Lei Wang, Jing Zhang","doi":"10.1145/3569966.3570108","DOIUrl":"https://doi.org/10.1145/3569966.3570108","url":null,"abstract":"Amazon Mechanical Turk (Mturk), as the world's largest micro-task crowdsourcing platform, provides task search services to thousands of users every day. However, the lack of navigation panel in the platform's existing user interface makes task searching difficult and tedious, resulting in users rarely finding acceptable tasks among the countless crowdsourced assignments. To lower the task search threshold of Mturk, this study creates an automated task navigation structure by combining the unsupervised task clustering algorithm and the topic recognition algorithm, which can be fine-tuned according to task characteristics and help users focus on the task type quickly and precisely. Since the navigation panel is a non-existent element of Mturk, this paper develops a well-designed questionnaire to investigate users' perceptions of the improved interface. The results show that users were dissatisfied with Mturk's current UI, preferring the navigation interface outlined in this study. In conclusion, this work provides theoretical guidance for building similar automated task navigation panels for other crowdsourcing platforms while addressing the practical problems of Mturk.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"39 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":"125665960","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}
Cross-modal tasks between text and images are increasingly a research hotspot. This paper proposed a cross-text-image generative adversarial network(CTI-GAN). This model can complete the cross-modal bidirectional generation task between image and text. The method effectively connects text and image modeling to realize bidirectional generation between image and text. The extraction effect of text features is improved by hierarchical LSTM encoding. Through feature pyramid fusion, the features of each layer are fully utilized to improve the image feature representation. In this paper, experiments are conducted to verify the effectiveness of the above improvements for image text generation. The improved algorithm can efficiently complete the task of cross-modal image text generation and improve the accuracy of the generated samples. In the text description generation image task, the inception score of CTI-GAN is improved by about 2% compared with StackGAN++, HDGAN, GAN-INT-CLS, and other models under the same conditions of the same dataset.
{"title":"CTI-GAN: Cross-Text-Image Generative Adversarial Network for Bidirectional Cross-modal Generation","authors":"Changhong Jing, Bing Xue, Ju-dong Pan","doi":"10.1145/3569966.3569990","DOIUrl":"https://doi.org/10.1145/3569966.3569990","url":null,"abstract":"Cross-modal tasks between text and images are increasingly a research hotspot. This paper proposed a cross-text-image generative adversarial network(CTI-GAN). This model can complete the cross-modal bidirectional generation task between image and text. The method effectively connects text and image modeling to realize bidirectional generation between image and text. The extraction effect of text features is improved by hierarchical LSTM encoding. Through feature pyramid fusion, the features of each layer are fully utilized to improve the image feature representation. In this paper, experiments are conducted to verify the effectiveness of the above improvements for image text generation. The improved algorithm can efficiently complete the task of cross-modal image text generation and improve the accuracy of the generated samples. In the text description generation image task, the inception score of CTI-GAN is improved by about 2% compared with StackGAN++, HDGAN, GAN-INT-CLS, and other models under the same conditions of the same dataset.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"48 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":"127470190","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}
Recent advances in three-generation sequencing technology allow for the rapid generation of large throughput of long reads, and mapping these long reads to a reference sequence is one of the first and most time-consuming steps in the downstream application of genomics. Minimap2, the state-of-the-art long-read sequencing aligner available today, has the advantage of being fast and accurate. However, as NUMA multi-core CPU gradually becomes the processors of mainstream computers, minimap2 is not specifically optimised and adapted for the NUMA multi-core architecture. Frequent remote memory accesses, resource contention and idle hardware resources result in a performance far below the theoretical peak performance of NUMA multi-core CPU. Based on the above problems, we propose three optimisation strategies, namely copying index at each NUMA node and binding threads to the cores of NUMA node, designing new IO and computation overlap mechanism, and adaptively adjusting batch_size based on IO and computation time, to achieve full utilisation of resources. We obtain three sets of human genome sequencing data from the ENA database and performed performance tests on the FT 2000+ MCD-FP92 NUMA multi-core CPU system. The three-point strategies proposed in this paper are effective in improving the performance of minimap2, with a maximum speedup of 13 percentage points.
{"title":"Accelerating minimap2 for long-read sequencing on NUMA multi-core CPU","authors":"Qisheng Xu, Y. Dou, Yanjie Sun","doi":"10.1145/3569966.3570012","DOIUrl":"https://doi.org/10.1145/3569966.3570012","url":null,"abstract":"Recent advances in three-generation sequencing technology allow for the rapid generation of large throughput of long reads, and mapping these long reads to a reference sequence is one of the first and most time-consuming steps in the downstream application of genomics. Minimap2, the state-of-the-art long-read sequencing aligner available today, has the advantage of being fast and accurate. However, as NUMA multi-core CPU gradually becomes the processors of mainstream computers, minimap2 is not specifically optimised and adapted for the NUMA multi-core architecture. Frequent remote memory accesses, resource contention and idle hardware resources result in a performance far below the theoretical peak performance of NUMA multi-core CPU. Based on the above problems, we propose three optimisation strategies, namely copying index at each NUMA node and binding threads to the cores of NUMA node, designing new IO and computation overlap mechanism, and adaptively adjusting batch_size based on IO and computation time, to achieve full utilisation of resources. We obtain three sets of human genome sequencing data from the ENA database and performed performance tests on the FT 2000+ MCD-FP92 NUMA multi-core CPU system. The three-point strategies proposed in this paper are effective in improving the performance of minimap2, with a maximum speedup of 13 percentage points.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"35 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":"127480251","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}
The traditional cable supply chain industry has problems such as difficulty in product traceability, production monitoring, rights protection and evidence collection, and data sharing, which seriously restrict the further development of the cable supply chain industry. A blockchain-based cable supply chain traceability system is designed using blockchain technology to integrate, share and supervise cable supply chain data. This system includes five functional modules: user management, data upload, two-way traceability, data sharing, and complaint handling. According to the characteristics of the cable traceability cycle, the storage module adopts a combination of blockchain, Inter-planetary File System (IPFS) and MySQL to meet different business needs. The experimental results show that the system basically meets the performance requirements and has good practicability.
{"title":"Blockchain-based cable supply chain traceability system","authors":"Xiaohong Qiu, Z. Tian","doi":"10.1145/3569966.3569977","DOIUrl":"https://doi.org/10.1145/3569966.3569977","url":null,"abstract":"The traditional cable supply chain industry has problems such as difficulty in product traceability, production monitoring, rights protection and evidence collection, and data sharing, which seriously restrict the further development of the cable supply chain industry. A blockchain-based cable supply chain traceability system is designed using blockchain technology to integrate, share and supervise cable supply chain data. This system includes five functional modules: user management, data upload, two-way traceability, data sharing, and complaint handling. According to the characteristics of the cable traceability cycle, the storage module adopts a combination of blockchain, Inter-planetary File System (IPFS) and MySQL to meet different business needs. The experimental results show that the system basically meets the performance requirements and has good practicability.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"486 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":"132693102","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}
Kun Qian, Yinghua Li, Xinggui Xu, Hao Xu, Guochang Hu, Ping-Hui Li
In general, the animation between three-dimensional models uses linear interpolation between models to calculate the intermediate state. Before interpolation, the mapping relationship between the source model and the target model should be calculated to find the one-to-one correspondence of vertices. It is often necessary to traverse all vertices of the mesh in interpolation calculation, it can be difficult to implement parallel operation due to the irregularity of triangular mesh. This paper is aimed to form a regular representation of the 3D model in the conformal parameterization and set up efficient parallel computing under the regular structure so as to improve the efficiency of deformation computing of 3D models.
{"title":"Efficient Parallel Computation Of 3D Model Deformation Based On Conformal Mapping","authors":"Kun Qian, Yinghua Li, Xinggui Xu, Hao Xu, Guochang Hu, Ping-Hui Li","doi":"10.1145/3569966.3570048","DOIUrl":"https://doi.org/10.1145/3569966.3570048","url":null,"abstract":"In general, the animation between three-dimensional models uses linear interpolation between models to calculate the intermediate state. Before interpolation, the mapping relationship between the source model and the target model should be calculated to find the one-to-one correspondence of vertices. It is often necessary to traverse all vertices of the mesh in interpolation calculation, it can be difficult to implement parallel operation due to the irregularity of triangular mesh. This paper is aimed to form a regular representation of the 3D model in the conformal parameterization and set up efficient parallel computing under the regular structure so as to improve the efficiency of deformation computing of 3D models.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"88 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":"133199287","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}
There has been an increasing interest in Deepfake detection because of the hidden risks that Deepfake technology poses for social privacy and security. Nowadays, many models achieve impressive performance on existing public benchmarks. However, the majority of existing methods are restricted to single-face scenarios. In this paper, we propose a model that can perform accurate and time-saving Deepfake detection in multi-face scenarios. We fuse different levels of features to improve the performance of the model and use single-face data to aid the training of the multi-face data. Our apporach achieves the state-of-the-art performance in multi-face scenarios and comprehensible experiments have been conducted to demonstrate the soundness and validity of our model.
{"title":"Accurate and Time-saving Deepfake Detection in Multi-face Scenarios Using Combined Features","authors":"Zekun Ma, B. Liu","doi":"10.1145/3569966.3570073","DOIUrl":"https://doi.org/10.1145/3569966.3570073","url":null,"abstract":"There has been an increasing interest in Deepfake detection because of the hidden risks that Deepfake technology poses for social privacy and security. Nowadays, many models achieve impressive performance on existing public benchmarks. However, the majority of existing methods are restricted to single-face scenarios. In this paper, we propose a model that can perform accurate and time-saving Deepfake detection in multi-face scenarios. We fuse different levels of features to improve the performance of the model and use single-face data to aid the training of the multi-face data. Our apporach achieves the state-of-the-art performance in multi-face scenarios and comprehensible experiments have been conducted to demonstrate the soundness and validity of our model.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"20 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":"113964063","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}
Based on Baidu Index and Internet Big Data, this paper analyzes the overall relationship of information flow spatial network by using social network method, and finds that index-time has dual structural characteristics and keeps changing trend. The research shows that the scale, correlation degree and control efficiency level of information flow show obvious structural characteristics of time dissimilation. Secondly, we study the behavior characteristics of index, dynamically analyze the temporal and spatial changes of big data of network attention and index dynamics, observe the inflow of index data in July and August, and find that the maximum value of a single index reaches 250,457 times. The inflow was the highest in the third quarter, with the maximum index reaching 659,329 times, showing the peak state of the whole period. Through information flow, the correlation between data is revealed, and the time distribution characteristics of monthly peak and quarterly peak are analyzed. Finally, it is concluded that the attention of information flow network has a clear direction in the two dimensions of "index-time", showing its correlation.
{"title":"The Exponential Dynamic Analysis of Network Attention Based on Big Data","authors":"Kaiyong Cheng, Fuxing Liang, Ling Xiao, Huiru Xu","doi":"10.1145/3569966.3570077","DOIUrl":"https://doi.org/10.1145/3569966.3570077","url":null,"abstract":"Based on Baidu Index and Internet Big Data, this paper analyzes the overall relationship of information flow spatial network by using social network method, and finds that index-time has dual structural characteristics and keeps changing trend. The research shows that the scale, correlation degree and control efficiency level of information flow show obvious structural characteristics of time dissimilation. Secondly, we study the behavior characteristics of index, dynamically analyze the temporal and spatial changes of big data of network attention and index dynamics, observe the inflow of index data in July and August, and find that the maximum value of a single index reaches 250,457 times. The inflow was the highest in the third quarter, with the maximum index reaching 659,329 times, showing the peak state of the whole period. Through information flow, the correlation between data is revealed, and the time distribution characteristics of monthly peak and quarterly peak are analyzed. Finally, it is concluded that the attention of information flow network has a clear direction in the two dimensions of \"index-time\", showing its correlation.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"18 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114006912","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}
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