Orthogonal Frequency Division Multiple Access-based Uplink Random Access (OFDMA-UORA) is a significant media access control mechanism in IEEE 802.11ax. An optimized OFDMA random access back-off (OBO) scheme is proposed to improve the performance of both light and heavy load networks. In the process of uplink random access,multiple users compete for multiple channels at the same time and follow an unknown joint Markov model. Users avoid collisions when competing for channels and maximize the throughput of the entire uplink process. The process can be formulated as a partially observable Markov decision process with unknown system dynamics. To this end, we apply the concepts of reinforcement learning and implement a deep q-network (DQN).Based on the original OBO mechanism, the OFDMA contention window size is dynamically decided via a deep reinforcement learning framework. For the proposed Deep Reinforcement Learning (DRL) solution, we design a discrete action agent that accommodates the contention window size by taking the channel and user state into account, e.g. the number of active users, available resources unit, and retries. Simulation results confirmed the advantages of the proposed scheme in throughput, delay, and access rate. This scheme can therefore be adopted in practical 802.11ax use cases to improve the network performance.
{"title":"Deep Reinforcement Learning for Dynamic OCW in UORA","authors":"Yong Hu, Zheng Guan, Tianyu Zhou","doi":"10.1145/3573834.3574522","DOIUrl":"https://doi.org/10.1145/3573834.3574522","url":null,"abstract":"Orthogonal Frequency Division Multiple Access-based Uplink Random Access (OFDMA-UORA) is a significant media access control mechanism in IEEE 802.11ax. An optimized OFDMA random access back-off (OBO) scheme is proposed to improve the performance of both light and heavy load networks. In the process of uplink random access,multiple users compete for multiple channels at the same time and follow an unknown joint Markov model. Users avoid collisions when competing for channels and maximize the throughput of the entire uplink process. The process can be formulated as a partially observable Markov decision process with unknown system dynamics. To this end, we apply the concepts of reinforcement learning and implement a deep q-network (DQN).Based on the original OBO mechanism, the OFDMA contention window size is dynamically decided via a deep reinforcement learning framework. For the proposed Deep Reinforcement Learning (DRL) solution, we design a discrete action agent that accommodates the contention window size by taking the channel and user state into account, e.g. the number of active users, available resources unit, and retries. Simulation results confirmed the advantages of the proposed scheme in throughput, delay, and access rate. This scheme can therefore be adopted in practical 802.11ax use cases to improve the network performance.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115165239","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}
China is a large agricultural country, and in the process of agricultural production, it is very important to make accurate prediction of soil moisture. To address the problems of local minimization and slow convergence of traditional BP (back propagation) neural network in the prediction process, this paper combines LSTM (long short-term memory) and Elman neural network with traditional BP neural network model, and proposes a method based on LSTM and Elman neural network for soil moisture prediction. A soil moisture prediction method based on LSTM and Elman neural network is proposed. The prediction model of LSTM and Elman neural network was developed, and the soil moisture of Xilinguole grassland in Inner Mongolia was predicted and experimented. The results show that the accuracy of the model is higher than that of the unoptimized BP neural network. The model is able to reduce the use of moisture sensors significantly, which reduces the cost for agricultural production.
{"title":"Soil moisture prediction model based on LSTM and Elman neural network","authors":"Luxia Ai, Xiang Sun, Qianman Zhang, Zhiqing Miao, Guangjie Li, Shaojing Song","doi":"10.1145/3573834.3574475","DOIUrl":"https://doi.org/10.1145/3573834.3574475","url":null,"abstract":"China is a large agricultural country, and in the process of agricultural production, it is very important to make accurate prediction of soil moisture. To address the problems of local minimization and slow convergence of traditional BP (back propagation) neural network in the prediction process, this paper combines LSTM (long short-term memory) and Elman neural network with traditional BP neural network model, and proposes a method based on LSTM and Elman neural network for soil moisture prediction. A soil moisture prediction method based on LSTM and Elman neural network is proposed. The prediction model of LSTM and Elman neural network was developed, and the soil moisture of Xilinguole grassland in Inner Mongolia was predicted and experimented. The results show that the accuracy of the model is higher than that of the unoptimized BP neural network. The model is able to reduce the use of moisture sensors significantly, which reduces the cost for agricultural production.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114163147","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}
Nowadays, the O-LLVM obfuscation framework makes it difficult to analyze various types of malware. To address this problem, this paper proposes a multi-architecture automated deobfuscation framework GOAMD specifically for O-LLVM obfuscation technology, which can intelligently identify the differences of programs on different architectures and perform targeted deobfuscation work on them. The experimental results show that the framework has high deobfuscation accuracy and portability.
{"title":"Generic O-LLVM Automatic Multi-architecture Deobfuscation Framework Based on Symbolic Execution","authors":"Yuhan Li, Bin Wen, Haixiao Zheng","doi":"10.1145/3573834.3574541","DOIUrl":"https://doi.org/10.1145/3573834.3574541","url":null,"abstract":"Nowadays, the O-LLVM obfuscation framework makes it difficult to analyze various types of malware. To address this problem, this paper proposes a multi-architecture automated deobfuscation framework GOAMD specifically for O-LLVM obfuscation technology, which can intelligently identify the differences of programs on different architectures and perform targeted deobfuscation work on them. The experimental results show that the framework has high deobfuscation accuracy and portability.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126499001","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}
In this paper, the problem of direction of arrival (DOA) estimation with sparse methods for array processing is concerned with the observation domain aspect, and an estimation method named beamspace-based sparse (BSE) is proposed. In BSE method, the beam space energy of the array signal is observed and modeled as the weighted sum of the signal energy of each azimuth beam pattern sequences of the conventional beamforming (CBF). BSE constructs a solution architecture for joint -norm minimization and quadratic constraint linear programming (QCLP) of noise power. Based on the estimation of noise background power under Gaussian noise conditions, a parameter selection method is derived, which can be quickly solved by the convex programming method. BSE has higher azimuth resolution and a lower false alarm rate when compared to sparse estimation methods based on other observation domains. It also performs well in coherent environments.
{"title":"A Beamspace-Based Sparse Estimation Method for Array Signal","authors":"Rongfeng Li, Xiaonan Xu, Yanyan An","doi":"10.1145/3573834.3574494","DOIUrl":"https://doi.org/10.1145/3573834.3574494","url":null,"abstract":"In this paper, the problem of direction of arrival (DOA) estimation with sparse methods for array processing is concerned with the observation domain aspect, and an estimation method named beamspace-based sparse (BSE) is proposed. In BSE method, the beam space energy of the array signal is observed and modeled as the weighted sum of the signal energy of each azimuth beam pattern sequences of the conventional beamforming (CBF). BSE constructs a solution architecture for joint -norm minimization and quadratic constraint linear programming (QCLP) of noise power. Based on the estimation of noise background power under Gaussian noise conditions, a parameter selection method is derived, which can be quickly solved by the convex programming method. BSE has higher azimuth resolution and a lower false alarm rate when compared to sparse estimation methods based on other observation domains. It also performs well in coherent environments.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121943161","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 avionics technology, the avionics architectures of aircraft has evolved from distributed analog architectures, to distributed digital architectures, then to federal digital architectures, and finally to integrated modular avionics(IMA) since the 1950s. IMA architecture is widely used in the design of modern commercial aircraft, which can optimize the information processing, communication and other functions of the avionics system, thereby improving the performance of the avionics system and reducing its energy consumption. This essay studies the composition and function of software components under the IMA framework, the method for determining the development assurance level (DAL) of each airborne software item, and forms a strategy for classifying airborne software based on DO-178C per DAL. Then identifies the technical concerns of different types of software in DO-178C application, establishes the IMA airborne software development process model and objective compliance verification model. The study results provide technical support for the development process assurance of airborne software under the IMA architecture.
{"title":"Study on the Application of RTCA/DO-178C in IMA Architecture","authors":"Lei Chen, J. Sun, She Jia","doi":"10.1145/3573834.3574470","DOIUrl":"https://doi.org/10.1145/3573834.3574470","url":null,"abstract":"With the development of avionics technology, the avionics architectures of aircraft has evolved from distributed analog architectures, to distributed digital architectures, then to federal digital architectures, and finally to integrated modular avionics(IMA) since the 1950s. IMA architecture is widely used in the design of modern commercial aircraft, which can optimize the information processing, communication and other functions of the avionics system, thereby improving the performance of the avionics system and reducing its energy consumption. This essay studies the composition and function of software components under the IMA framework, the method for determining the development assurance level (DAL) of each airborne software item, and forms a strategy for classifying airborne software based on DO-178C per DAL. Then identifies the technical concerns of different types of software in DO-178C application, establishes the IMA airborne software development process model and objective compliance verification model. The study results provide technical support for the development process assurance of airborne software under the IMA architecture.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122211650","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}
Extreme learning machine (ELM) is a network model that arbitrarily initializes the first hidden layer and can be computed speedily. In order to improve the classification performance of ELM, a ℓ2 and ℓ0.5 regularization ELM model (ℓ2-ℓ0.5-ELM) is proposed in this paper. An iterative optimization algorithm of the fixed point contraction mapping is applied to solve the ℓ2-ℓ0.5-ELM model. The convergence and sparsity of the proposed method are discussed and analyzed under reasonable assumptions. The performance of the proposed ℓ2-ℓ0.5-ELM method is compared with BP, SVM, ELM, ℓ0.5-ELM, ℓ1-ELM, ℓ2-ELM and ℓ2-ℓ1ELM, the results show that the prediction accuracy, sparsity, and stability of the ℓ2-ℓ0.5-ELM are better than the other 7 models.
{"title":"An improved hybrid regularization approach for extreme learning machine","authors":"Liangjuan Zhou, Wei Miao","doi":"10.1145/3573834.3574501","DOIUrl":"https://doi.org/10.1145/3573834.3574501","url":null,"abstract":"Extreme learning machine (ELM) is a network model that arbitrarily initializes the first hidden layer and can be computed speedily. In order to improve the classification performance of ELM, a ℓ2 and ℓ0.5 regularization ELM model (ℓ2-ℓ0.5-ELM) is proposed in this paper. An iterative optimization algorithm of the fixed point contraction mapping is applied to solve the ℓ2-ℓ0.5-ELM model. The convergence and sparsity of the proposed method are discussed and analyzed under reasonable assumptions. The performance of the proposed ℓ2-ℓ0.5-ELM method is compared with BP, SVM, ELM, ℓ0.5-ELM, ℓ1-ELM, ℓ2-ELM and ℓ2-ℓ1ELM, the results show that the prediction accuracy, sparsity, and stability of the ℓ2-ℓ0.5-ELM are better than the other 7 models.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"415 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117302313","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 load prediction of host resources is a key issue to enhance the cloud computing aid allocation system. With the change of cloud computing resource load displaying extra and extra complicated characteristics, traditional prediction algorithms can solely predict the linear traits of data, and it is tough to precisely predict useful resource usage. In order to enhance the forecasting accuracy of the model, a blended load forecasting algorithm based totally on machine learning is proposed. The machine learning prediction model can nicely match the nonlinear traits of the data. The linear phase of the algorithm makes use of ARIMA prediction, and the nonlinear section makes use of particle swarm optimization algorithm to optimize LSTM prediction. Then, the optimal least squares method is used to redistribute the prediction error weights of the autoregressive differential moving average model (ARIMA) and the long-term and short-term memory network models (LSTM), and finally the prediction results are output. The comparison experiment is carried out with the open actual load data set. The experimental results show that the prediction accuracy of the weight redistribution combination model is significantly higher than that of other traditional prediction models and machine learning prediction models when the prediction time efficiency is similar, and the real-time prediction error of resource load in cloud environment is significantly reduced.
{"title":"Load prediction optimization based on machine learning in cloud computing environment","authors":"Guozheng Feng, Jianbo Xu, Wei Jian, Zhang Liu","doi":"10.1145/3573834.3574511","DOIUrl":"https://doi.org/10.1145/3573834.3574511","url":null,"abstract":"The load prediction of host resources is a key issue to enhance the cloud computing aid allocation system. With the change of cloud computing resource load displaying extra and extra complicated characteristics, traditional prediction algorithms can solely predict the linear traits of data, and it is tough to precisely predict useful resource usage. In order to enhance the forecasting accuracy of the model, a blended load forecasting algorithm based totally on machine learning is proposed. The machine learning prediction model can nicely match the nonlinear traits of the data. The linear phase of the algorithm makes use of ARIMA prediction, and the nonlinear section makes use of particle swarm optimization algorithm to optimize LSTM prediction. Then, the optimal least squares method is used to redistribute the prediction error weights of the autoregressive differential moving average model (ARIMA) and the long-term and short-term memory network models (LSTM), and finally the prediction results are output. The comparison experiment is carried out with the open actual load data set. The experimental results show that the prediction accuracy of the weight redistribution combination model is significantly higher than that of other traditional prediction models and machine learning prediction models when the prediction time efficiency is similar, and the real-time prediction error of resource load in cloud environment is significantly reduced.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131200871","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 rapid growth oftrajectory data has prompted researchers to develop multiple large trajectory data management systems. One of the fundamental requirements of all these systems, regardless of their architecture, is to partition data efficiently between machines. In the typical query operations of tracks, the query on ID is a frequent operation of track query, such as ID time range query, ID space range query, etc. A widely used ID indexing technique is to reuse an existing search tree, such as a Kd-tree, by building a temporary tree for the input samples and using its leaf nodes as partition boundaries. However, we show in this paper that this approach has significant limitations. To overcome these limitations, we propose a new indexing, BOD-tree, which inherits the main features of the Kd-tree and can also partition the dataset into multiple balanced splits. We test the method on real datasets, and extensive experiments show that our algorithm can improve resource usage efficiency.
{"title":"BOD-tree: An One-Dimensional Balanced Indexing Algorithm","authors":"Ruijie Tian, Weishi Zhang, Fei Wang","doi":"10.1145/3573834.3574493","DOIUrl":"https://doi.org/10.1145/3573834.3574493","url":null,"abstract":"The rapid growth oftrajectory data has prompted researchers to develop multiple large trajectory data management systems. One of the fundamental requirements of all these systems, regardless of their architecture, is to partition data efficiently between machines. In the typical query operations of tracks, the query on ID is a frequent operation of track query, such as ID time range query, ID space range query, etc. A widely used ID indexing technique is to reuse an existing search tree, such as a Kd-tree, by building a temporary tree for the input samples and using its leaf nodes as partition boundaries. However, we show in this paper that this approach has significant limitations. To overcome these limitations, we propose a new indexing, BOD-tree, which inherits the main features of the Kd-tree and can also partition the dataset into multiple balanced splits. We test the method on real datasets, and extensive experiments show that our algorithm can improve resource usage efficiency.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127801972","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}
Wetlands are very complex yet pivotal ecosystems on Earth. They serve as habitats for various flora and fauna. Alongside, wetlands are crucial for biogeochemical exchange between the Earth’s surface and its atmosphere. A large proportion of organic carbon is sequestered in wetlands and plays a substantial role in the carbon cycle. The planning and management of wetlands depend a lot upon a reliable wetland model. The underlying complex dynamics of wetlands hinder the modelling of wetland extent. This study for the first time considers multivariate nonlinear dynamical system modelling using Nonlinear Autoregressive with Exogenous Inputs (NARX) model class. The data consists of weather variables and wetland fractions for two wetland sites falling under Asia and Africa. The model is simulated using fresh testing data and can predict wetland extent satisfactorily for both sample sites. The accuracy of the models is quantified using Root Mean square Error (RMSE) and Mean Absolute Error (MAE). A transparent NARX structure reveals the dynamical elements for the potential planning and management of wetlands.
{"title":"Nonlinear multivariate modelling of wetland dynamics","authors":"Angesh Anupam","doi":"10.1145/3573834.3574500","DOIUrl":"https://doi.org/10.1145/3573834.3574500","url":null,"abstract":"Wetlands are very complex yet pivotal ecosystems on Earth. They serve as habitats for various flora and fauna. Alongside, wetlands are crucial for biogeochemical exchange between the Earth’s surface and its atmosphere. A large proportion of organic carbon is sequestered in wetlands and plays a substantial role in the carbon cycle. The planning and management of wetlands depend a lot upon a reliable wetland model. The underlying complex dynamics of wetlands hinder the modelling of wetland extent. This study for the first time considers multivariate nonlinear dynamical system modelling using Nonlinear Autoregressive with Exogenous Inputs (NARX) model class. The data consists of weather variables and wetland fractions for two wetland sites falling under Asia and Africa. The model is simulated using fresh testing data and can predict wetland extent satisfactorily for both sample sites. The accuracy of the models is quantified using Root Mean square Error (RMSE) and Mean Absolute Error (MAE). A transparent NARX structure reveals the dynamical elements for the potential planning and management of wetlands.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133794998","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}
Network traffic plays a crucial role in the interaction and transfer of information in the network area, which contains a large amount of information with important value. Therefore, network traffic classification is essential for network management, security monitoring and intrusion detection. However, the performance of network traffic classification is greatly affected by the extremely unbalanced datasets which are publicly available. In order to solve the problem of low accuracy of minority class classification. In this paper, we used SMOTEENN as the balanced method and XGBOOST_RFECV was used for feature selection. Subsequently, the neural network model (1DCNN_BiLSTM) was used for training and verification. The experimental results show that this method can effectively solve the problem of imbalanced data category, which has certain reference significance for the research of network traffic classification technology.
{"title":"A Network Traffic Classification Model Based On XGBOOST_RFECV Feature Extraction","authors":"Ming Li, Guikai Liu","doi":"10.1145/3573834.3574543","DOIUrl":"https://doi.org/10.1145/3573834.3574543","url":null,"abstract":"Network traffic plays a crucial role in the interaction and transfer of information in the network area, which contains a large amount of information with important value. Therefore, network traffic classification is essential for network management, security monitoring and intrusion detection. However, the performance of network traffic classification is greatly affected by the extremely unbalanced datasets which are publicly available. In order to solve the problem of low accuracy of minority class classification. In this paper, we used SMOTEENN as the balanced method and XGBOOST_RFECV was used for feature selection. Subsequently, the neural network model (1DCNN_BiLSTM) was used for training and verification. The experimental results show that this method can effectively solve the problem of imbalanced data category, which has certain reference significance for the research of network traffic classification technology.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115111779","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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