Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00014
Xiangyu Gao, M. Qiu
Big data analysis has become an essential tool in a lot of fields. An increasing number of entities rely on different kinds of data analysis tools to formulate their strategy. However, the popularity of big data brings several problems as well because attackers might pollute the data set by adding negligible data points to make a negative effect on the final analysis results. Therefore, in this paper, we propose to leverage the energy-based learning method to detect outliers within a data set. Specifically, we iteratively rule out bad data points from the data set based on specific selection rules. The experiment result is promising, which shows that our algorithm can improve the accuracy in the linear regression by more than 20% on average.
{"title":"Energy-Based Learning for Polluted Outlier Detection in Backdoor","authors":"Xiangyu Gao, M. Qiu","doi":"10.1109/SmartCloud55982.2022.00014","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00014","url":null,"abstract":"Big data analysis has become an essential tool in a lot of fields. An increasing number of entities rely on different kinds of data analysis tools to formulate their strategy. However, the popularity of big data brings several problems as well because attackers might pollute the data set by adding negligible data points to make a negative effect on the final analysis results. Therefore, in this paper, we propose to leverage the energy-based learning method to detect outliers within a data set. Specifically, we iteratively rule out bad data points from the data set based on specific selection rules. The experiment result is promising, which shows that our algorithm can improve the accuracy in the linear regression by more than 20% on average.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126940321","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}
With the development of edge computing and cloud computing in power scenarios, the cloud center collects a large amount of data from edge nodes every day, and the load of edge nodes is overloaded and the transmission delay increases, making it difficult to store and use data in the cloud center. The communication capabilities, storage capabilities and computing capabilities of nodes face greater challenges. Load data is the most important structural data in the asynchronous heterogeneous data of electric power. In order to reduce the amount of data generated during the transmission process of the edge network, compression technology can be used to effectively compress the load data. Before using the traditional integrated neural network model to compress the time series load data, it is necessary to calculate the variance of the window data, and compare the obtained variance with the empirical threshold, so as to divide the load data into stable data and unstable data. Due to the complex logic of data preprocessing, the overall compression calculation is time-consuming, and the robustness of the data classification algorithm is not high due to the need to manually set empirical parameters. In this paper, the multilayer perceptron is applied to load data classification, combined with the integrated neural network model, to construct an edge-side data compression scheme that can be applied to smart grid scenarios. This scheme achieves faster compression speed on the basis of ensuring the original compression ratio.
{"title":"Electric Power Asynchronous Heterogeneous Data Accelerated Compression for Edge Computing","authors":"Hongkai Wang, Hanyu Rao, Xiaogang Gong, Zuge Chen, Dong Mao, Jingyao Zhang","doi":"10.1109/SmartCloud55982.2022.00025","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00025","url":null,"abstract":"With the development of edge computing and cloud computing in power scenarios, the cloud center collects a large amount of data from edge nodes every day, and the load of edge nodes is overloaded and the transmission delay increases, making it difficult to store and use data in the cloud center. The communication capabilities, storage capabilities and computing capabilities of nodes face greater challenges. Load data is the most important structural data in the asynchronous heterogeneous data of electric power. In order to reduce the amount of data generated during the transmission process of the edge network, compression technology can be used to effectively compress the load data. Before using the traditional integrated neural network model to compress the time series load data, it is necessary to calculate the variance of the window data, and compare the obtained variance with the empirical threshold, so as to divide the load data into stable data and unstable data. Due to the complex logic of data preprocessing, the overall compression calculation is time-consuming, and the robustness of the data classification algorithm is not high due to the need to manually set empirical parameters. In this paper, the multilayer perceptron is applied to load data classification, combined with the integrated neural network model, to construct an edge-side data compression scheme that can be applied to smart grid scenarios. This scheme achieves faster compression speed on the basis of ensuring the original compression ratio.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124980855","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}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00035
Hong-an Li, Diao Wang, Zhanli Li, Tian Ma
Image super-resolution reconstruction can reconstruct low-resolution images into high-resolution images, which is an important application of big data combined with cloud computing. Using big data technology can mine the useful information of a large number of images, and cloud computing can reduce the model computation. However, existing super-resolution models are difficult to train and have problems such as artifacts, blurred detail texture and too smooth after image reconstruction. To solve the above problems, we propose the Multi-scale double Attention mechanism based on Residual Dense Generative Adversarial Network (MARDGAN), which uses multi-branch paths to extract image features of different scale sizes, to obtain multi-scale features information. We also design the double attention mechanism block (CSAB) and combine it with the Enhanced Residual Dense Block (ERDB) to form the deep residual dense attention module (DRDAM) to extract multi-level depth feature information. The perceptual capability of the model is improved by adding pixel loss, perceptual loss, and adversarial loss. The experimental results show that our proposed MARDGAN has shorter training time. And it can use the original image information more effectively than other methods on multiple benchmark datasets to recover super-resolution images with clearer details and better realism.
{"title":"Image Super-Resolution Reconstruction Based on Big Data and Cloud Computing","authors":"Hong-an Li, Diao Wang, Zhanli Li, Tian Ma","doi":"10.1109/SmartCloud55982.2022.00035","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00035","url":null,"abstract":"Image super-resolution reconstruction can reconstruct low-resolution images into high-resolution images, which is an important application of big data combined with cloud computing. Using big data technology can mine the useful information of a large number of images, and cloud computing can reduce the model computation. However, existing super-resolution models are difficult to train and have problems such as artifacts, blurred detail texture and too smooth after image reconstruction. To solve the above problems, we propose the Multi-scale double Attention mechanism based on Residual Dense Generative Adversarial Network (MARDGAN), which uses multi-branch paths to extract image features of different scale sizes, to obtain multi-scale features information. We also design the double attention mechanism block (CSAB) and combine it with the Enhanced Residual Dense Block (ERDB) to form the deep residual dense attention module (DRDAM) to extract multi-level depth feature information. The perceptual capability of the model is improved by adding pixel loss, perceptual loss, and adversarial loss. The experimental results show that our proposed MARDGAN has shorter training time. And it can use the original image information more effectively than other methods on multiple benchmark datasets to recover super-resolution images with clearer details and better realism.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122067121","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}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00034
Fang Li, Gang Wu, Jianhua Lu, Mingye Jin, Haitao An, Junxiong Lin
Cloud computing, as a new format of the information industry, is the key technology and means to lead the innovation and development of the information industry in the future. As the global economy continues to decline, enterprises pay more attention to refined operations and cost reduction and efficiency enhancement than before, and cloud computing is no exception. Does cloud computing ultimately increase the cost of business or is it worth the money? In the situation of business homogeneity competition, the cost, investment and operation of cloud infrastructure have also become the key to affecting the market competitiveness of enterprise cloud business. To achieve these issues, we design the platform named SmartCMP, which provides the value of cloud cost analysis and optimization. Firstly, from the perspective of financial, multi-dimensional display and analysis of cloud costs are carried out to find ways to reduce costs. Then, users can customize strategies for daily operation, speed up decision-making and reduce risks. Lastly, our platform will actively monitor, detect and repair risks in real time in accordance with policies to strengthen security. The effectiveness of our platform can be verified by comparing to other strategies.
{"title":"SmartCMP: A Cloud Cost Optimization Governance Practice of Smart Cloud Management Platform","authors":"Fang Li, Gang Wu, Jianhua Lu, Mingye Jin, Haitao An, Junxiong Lin","doi":"10.1109/SmartCloud55982.2022.00034","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00034","url":null,"abstract":"Cloud computing, as a new format of the information industry, is the key technology and means to lead the innovation and development of the information industry in the future. As the global economy continues to decline, enterprises pay more attention to refined operations and cost reduction and efficiency enhancement than before, and cloud computing is no exception. Does cloud computing ultimately increase the cost of business or is it worth the money? In the situation of business homogeneity competition, the cost, investment and operation of cloud infrastructure have also become the key to affecting the market competitiveness of enterprise cloud business. To achieve these issues, we design the platform named SmartCMP, which provides the value of cloud cost analysis and optimization. Firstly, from the perspective of financial, multi-dimensional display and analysis of cloud costs are carried out to find ways to reduce costs. Then, users can customize strategies for daily operation, speed up decision-making and reduce risks. Lastly, our platform will actively monitor, detect and repair risks in real time in accordance with policies to strengthen security. The effectiveness of our platform can be verified by comparing to other strategies.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131300165","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}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00008
Bruno Guindani, D. Ardagna, A. Guglielmi
Bayesian optimization (BO) is an efficient method for finding optimal cloud computing configurations for several types of applications. On the other hand, Machine Learning (ML) methods can provide useful knowledge about the application at hand thanks to their predicting capabilities. In this paper, we propose a hybrid algorithm that is based on BO and integrates elements from ML techniques, to find the optimal configuration of time-constrained recurring jobs executed in cloud environments. The algorithm is tested by considering edge computing and Apache Spark big data applications. The results we achieve show that our approach reduces the amount of unfeasible executions up to 2-3 times with respect to state-of-the-art techniques.
{"title":"MALIBOO: When Machine Learning meets Bayesian Optimization","authors":"Bruno Guindani, D. Ardagna, A. Guglielmi","doi":"10.1109/SmartCloud55982.2022.00008","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00008","url":null,"abstract":"Bayesian optimization (BO) is an efficient method for finding optimal cloud computing configurations for several types of applications. On the other hand, Machine Learning (ML) methods can provide useful knowledge about the application at hand thanks to their predicting capabilities. In this paper, we propose a hybrid algorithm that is based on BO and integrates elements from ML techniques, to find the optimal configuration of time-constrained recurring jobs executed in cloud environments. The algorithm is tested by considering edge computing and Apache Spark big data applications. The results we achieve show that our approach reduces the amount of unfeasible executions up to 2-3 times with respect to state-of-the-art techniques.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"81 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130996053","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}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00041
Hongshan Yang, Wanggen Liu, Chenyun Liu, Tianqing Wang, Lei Peng
Large value hidden in Big Data has motivated enterprises to transform from information technology magnates to data technology magnates. Cloud computing and artificial intelligence offer eligible solutions for extracting information in Big Data, and integration of the above three renders the data cloud platform. In this paper, we specify Transwarp Data Cloud (TDC), a stable, efficient, and intelligent data application cloud platform, which fuses data cloud, analytics cloud, and application cloud to manage all types of applications and services while sharing a well-maintained underlying infrastructure layer. In addition, we identify a general application framework based on TDC, by which various data-driven applications can be supported, and then we discuss some popular applied domains including Big Data, Application Development and Management, and artificial intelligence. With powerful tools and flexible application support, TDC can help enterprises with the innovation and revolution of business in the era of Big Data.
大数据蕴含的巨大价值促使企业从信息技术巨头向数据技术巨头转型。云计算和人工智能为大数据中的信息提取提供了合适的解决方案，三者的融合构成了数据云平台。Transwarp Data Cloud (TDC)是一个稳定、高效、智能的数据应用云平台，它融合了数据云、分析云和应用云来管理所有类型的应用和服务，同时共享一个维护良好的底层基础架构层。此外，我们确定了一个基于TDC的通用应用框架，通过它可以支持各种数据驱动的应用，然后我们讨论了一些流行的应用领域，包括大数据、应用开发与管理和人工智能。凭借强大的工具和灵活的应用支持，TDC可以帮助企业在大数据时代进行商业创新和革命。
{"title":"Transwarp Data Cloud: A Stable, Efficient, and Intelligent Data Application Cloud Platform","authors":"Hongshan Yang, Wanggen Liu, Chenyun Liu, Tianqing Wang, Lei Peng","doi":"10.1109/SmartCloud55982.2022.00041","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00041","url":null,"abstract":"Large value hidden in Big Data has motivated enterprises to transform from information technology magnates to data technology magnates. Cloud computing and artificial intelligence offer eligible solutions for extracting information in Big Data, and integration of the above three renders the data cloud platform. In this paper, we specify Transwarp Data Cloud (TDC), a stable, efficient, and intelligent data application cloud platform, which fuses data cloud, analytics cloud, and application cloud to manage all types of applications and services while sharing a well-maintained underlying infrastructure layer. In addition, we identify a general application framework based on TDC, by which various data-driven applications can be supported, and then we discuss some popular applied domains including Big Data, Application Development and Management, and artificial intelligence. With powerful tools and flexible application support, TDC can help enterprises with the innovation and revolution of business in the era of Big Data.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132249782","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}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00044
Ajay Katangur, S. Akkaladevi, Sadiskumar Vivekanandhan
Cloud computing, which helps in sharing resources through networks, has become one of the most widely used technologies in recent years. Vast numbers of organizations are moving to the cloud since it is more cost-effective and easy to maintain. An increase in the number of consumers using the cloud, however, results in increased traffic, which leads to the problem of balancing tasks on the loads. Numerous dynamic algorithms [1] have been proposed and implemented to handle these loads in different ways. The performance of these dynamic algorithms are scaled with different parameters, such as response time, throughput, utilization, efficiency, etc. The weighted round-robin algorithm is one of the most widely used load balancing algorithms. The proposed algorithm is an improvement of the weighted round-robin algorithm, which considers the priority of every task before assigning the tasks to different virtual machines (VMs). The proposed algorithm uses the priority of tasks to decide to which VMs the tasks should be assigned dynamically. The same process is used to migrate the tasks from overloaded VMs to under-loaded VMs. The simulations are conducted using CloudSim by varying cloud resources. Simulation results show that the proposed algorithm performs equivalent to the dynamic weighted round robin algorithm in all the QoS factors, but it shows significant improvement in handling high-priority tasks.
{"title":"Priority Weighted Round Robin Algorithm for Load Balancing in the Cloud","authors":"Ajay Katangur, S. Akkaladevi, Sadiskumar Vivekanandhan","doi":"10.1109/SmartCloud55982.2022.00044","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00044","url":null,"abstract":"Cloud computing, which helps in sharing resources through networks, has become one of the most widely used technologies in recent years. Vast numbers of organizations are moving to the cloud since it is more cost-effective and easy to maintain. An increase in the number of consumers using the cloud, however, results in increased traffic, which leads to the problem of balancing tasks on the loads. Numerous dynamic algorithms [1] have been proposed and implemented to handle these loads in different ways. The performance of these dynamic algorithms are scaled with different parameters, such as response time, throughput, utilization, efficiency, etc. The weighted round-robin algorithm is one of the most widely used load balancing algorithms. The proposed algorithm is an improvement of the weighted round-robin algorithm, which considers the priority of every task before assigning the tasks to different virtual machines (VMs). The proposed algorithm uses the priority of tasks to decide to which VMs the tasks should be assigned dynamically. The same process is used to migrate the tasks from overloaded VMs to under-loaded VMs. The simulations are conducted using CloudSim by varying cloud resources. Simulation results show that the proposed algorithm performs equivalent to the dynamic weighted round robin algorithm in all the QoS factors, but it shows significant improvement in handling high-priority tasks.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134334487","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}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00021
Pengyu Zhang
The understanding of text with numerical values is now involved in many application areas and the extraction of numerically relevant and important information from unstructured data is a hot topic of research. The main work in this paper is divided into three parts. The first part is a specification of Chinese numerical fact extraction and annotation, using numerical values as the core of the annotation to find other important information, where numerically measured entities and attributes are the main targets. The second part is the design of extraction methods, using two methods based on deep learning of different task forms as extraction models, namely the NER Combine and Quantity MRC methods. The former uses the sequence annotation task to extract fields, and the combination algorithm based on field distance connects values with other information; The latter uses machine reading comprehension to find its counterpart in other information by introducing numerical information as interrogative sentences. The aim of designing a supervised algorithm based on deep learning is to find the desired target more accurately than an unsupervised algorithm, to avoid the problem of having to exhaust a large number of rules to deal with trivial situations in an unsupervised algorithm, and to benefit from the a priori knowledge and strong representational power of the pre-trained language model to improve the robustness and usability of the extraction results. The third part is experimental verification, which shows the advantages and disadvantages of the two extraction methods in different contexts.
{"title":"A Numerical Fact Extraction Method for Chinese Text","authors":"Pengyu Zhang","doi":"10.1109/SmartCloud55982.2022.00021","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00021","url":null,"abstract":"The understanding of text with numerical values is now involved in many application areas and the extraction of numerically relevant and important information from unstructured data is a hot topic of research. The main work in this paper is divided into three parts. The first part is a specification of Chinese numerical fact extraction and annotation, using numerical values as the core of the annotation to find other important information, where numerically measured entities and attributes are the main targets. The second part is the design of extraction methods, using two methods based on deep learning of different task forms as extraction models, namely the NER Combine and Quantity MRC methods. The former uses the sequence annotation task to extract fields, and the combination algorithm based on field distance connects values with other information; The latter uses machine reading comprehension to find its counterpart in other information by introducing numerical information as interrogative sentences. The aim of designing a supervised algorithm based on deep learning is to find the desired target more accurately than an unsupervised algorithm, to avoid the problem of having to exhaust a large number of rules to deal with trivial situations in an unsupervised algorithm, and to benefit from the a priori knowledge and strong representational power of the pre-trained language model to improve the robustness and usability of the extraction results. The third part is experimental verification, which shows the advantages and disadvantages of the two extraction methods in different contexts.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133268525","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}
Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00029
Feng Zhou, Xiaoli Wan, Xin Du, Zhihui Lu, Jie Wu
The ageing population has led to a dramatic increase in the demand for analysis and assessment of the health of older persons in public health services. Due to medical conditions and other reasons, most of the elderly in some urban nursing homes will only detect and analyze their own physiological indicators when they are sick. From the perspective of health management, we should continuously monitor the physiological indicators of each elderly individual, and through the analysis and evaluation of their daily physiological indicators data, and then predict and timely intervene in their health. This can not only effectively improve the health of the elderly, but also effectively reduce the pressure on public health services. In order to allow more elderly people in nursing homes to enjoy effective health monitoring and early warning and timely intervention, we have designed an intelligent health management system based on technologies such as cloud computing, Internet of Things, knowledge graph, and deep learning. The system consists of three parts: the Internet of Things platform, the intelligent analysis platform, and the SAAS management platform. The IoT platform is mainly responsible for collecting data such as daily physiological indicators, sleep data, air indicators, and service demands of elderly people in nursing homes. The intelligent analysis platform is mainly responsible for analyzing and evaluating the data collected by the IoT platform based on the disease knowledge map and related deep learning frameworks. The SAAS management platform is mainly responsible for background management and health data visualization on the nursing terminal, service terminal, and health monitoring terminal. The system realizes continuous monitoring, analysis, assessment, prediction and early intervention of the health of each elderly person in the nursing home, which effectively improves the health of the elderly and effectively reduces the pressure on public health services.
{"title":"Design and Implementation of An Intelligent Health Management System for Nursing Homes","authors":"Feng Zhou, Xiaoli Wan, Xin Du, Zhihui Lu, Jie Wu","doi":"10.1109/SmartCloud55982.2022.00029","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00029","url":null,"abstract":"The ageing population has led to a dramatic increase in the demand for analysis and assessment of the health of older persons in public health services. Due to medical conditions and other reasons, most of the elderly in some urban nursing homes will only detect and analyze their own physiological indicators when they are sick. From the perspective of health management, we should continuously monitor the physiological indicators of each elderly individual, and through the analysis and evaluation of their daily physiological indicators data, and then predict and timely intervene in their health. This can not only effectively improve the health of the elderly, but also effectively reduce the pressure on public health services. In order to allow more elderly people in nursing homes to enjoy effective health monitoring and early warning and timely intervention, we have designed an intelligent health management system based on technologies such as cloud computing, Internet of Things, knowledge graph, and deep learning. The system consists of three parts: the Internet of Things platform, the intelligent analysis platform, and the SAAS management platform. The IoT platform is mainly responsible for collecting data such as daily physiological indicators, sleep data, air indicators, and service demands of elderly people in nursing homes. The intelligent analysis platform is mainly responsible for analyzing and evaluating the data collected by the IoT platform based on the disease knowledge map and related deep learning frameworks. The SAAS management platform is mainly responsible for background management and health data visualization on the nursing terminal, service terminal, and health monitoring terminal. The system realizes continuous monitoring, analysis, assessment, prediction and early intervention of the health of each elderly person in the nursing home, which effectively improves the health of the elderly and effectively reduces the pressure on public health services.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124225343","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}
Facing the complex network security environment, it is particularly important to quickly obtain the latest network threat intelligence to identify, block and track network attacks. How to obtain network threat intelligence data has become a key research content. Named entity recognition (NER) technology provides ideas to solve this problem. Firstly, this paper summarizes the methods of named entity recognition, then introduces the research status of NER in the field of Chinese, then introduces the latest research results of NER in the field of network security, and finally summarizes the challenges encountered in related tasks and the prospect of future research.
{"title":"Survey of Research on Named Entity Recognition in Cyber Threat Intelligence","authors":"Keke Zhang, Xu Chen, Yongjun Jing, Shuyang Wang, Lijun Tang","doi":"10.1109/SmartCloud55982.2022.00017","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00017","url":null,"abstract":"Facing the complex network security environment, it is particularly important to quickly obtain the latest network threat intelligence to identify, block and track network attacks. How to obtain network threat intelligence data has become a key research content. Named entity recognition (NER) technology provides ideas to solve this problem. Firstly, this paper summarizes the methods of named entity recognition, then introduces the research status of NER in the field of Chinese, then introduces the latest research results of NER in the field of network security, and finally summarizes the challenges encountered in related tasks and the prospect of future research.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115885054","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：