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2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)最新文献

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Research on Network Element Management Model Based on Cloud Native Technology 基于云原生技术的网元管理模型研究
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807784
Yuting Wu, Xuliang Wang
{"title":"Research on Network Element Management Model Based on Cloud Native Technology","authors":"Yuting Wu, Xuliang Wang","doi":"10.1109/CCAI55564.2022.9807784","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807784","url":null,"abstract":"With the promotion and development of cloud-native ideas and technologies, cloud-native has gradually matured and entered the application stage, and cloud-native technologies have also received extensive attention from operators. In this context, based on cloud native technology, this paper proposes a management model for CNF (Cloud Native Network Function). The model is based on cloud native ideas, using cloud native declarative API features, Kubernetes’ CRD (Custom Resource Definition) and the Controller mechanism and the Side-Car design mode to realize the automatic management of cloud-native network functions. This management method is consistent with the deployment and arrangement of network functions in the form of API, providing better user experience and more efficient use.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123295581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
TATCN: Time Series Prediction Model Based on Time Attention Mechanism and TCN 基于时间注意机制和TCN的时间序列预测模型
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807714
Hongya Wang, Zhenguo Zhang
{"title":"TATCN: Time Series Prediction Model Based on Time Attention Mechanism and TCN","authors":"Hongya Wang, Zhenguo Zhang","doi":"10.1109/CCAI55564.2022.9807714","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807714","url":null,"abstract":"Prediction is an important research task of time series data analysis. As a powerful tool to solve the problem of time series prediction, Temporal Convolutional Networks (TCN) shows good performance in the prediction task. However, TCN model lacks the consideration of the influence of different historical segments on the prediction value, which limits the prediction accuracy of the model to a certain extent. Therefore, this paper combines the attention mechanism with the data characteristics of time series, proposes a Time Attention mechanism (TA), and integrates it into the TCN model framework to build a prediction model (called TATCN). In TATCN, the TCN output vector of each layer is convoluted, and the sigmoid function is used to generate the weight coefficient, and then the weight coefficient is multiplied by the original output vector to form a new output vector. The new output vector and the input vector of the current layer are added by residual connection as the final output vector of the current layer and input to the next layer network. The experimental results on EEG data and Yanbian electricity fees data show that the Time Attention mechanism in this paper can effectively represent the importance of different historical data to the current prediction. The proposed TATCN model has a significant improvement in the prediction accuracy compared with TCN model, and is also better than RNN prediction models such as LSTM and GRU.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121403954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Data-Efficient Method for One-Shot Text Classification 一种数据高效的一次性文本分类方法
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807798
H. Wang, Mu Liu, Katsushi Yamashita, Yasuhiro Okamoto, Satoshi Yamada
{"title":"A Data-Efficient Method for One-Shot Text Classification","authors":"H. Wang, Mu Liu, Katsushi Yamashita, Yasuhiro Okamoto, Satoshi Yamada","doi":"10.1109/CCAI55564.2022.9807798","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807798","url":null,"abstract":"In this paper, we propose BiGBERT (Binary Grouping BERT), a data-efficient training method for one-shot text classification. With the idea of One-vs-Rest method, we designed an extensible output layer for BERT, which can increase the usability of the training data. To evaluate our approach, we conducted extensive experiments on four celebrated text classification datasets, and reform these datasets into one-shot training scenario, which is approximately equal to the situation of our commercial datasets. The experiment result shows our approach achieves 54.9% in 5AbstractsGroup dataset, 40.2% in 20NewsGroup dataset, 57.0% in IMDB dataset, and 33.6% in TREC dataset. Overall, compare to the baseline BERT, our proposed method achieves 2.3% $sim$ 28.6% improved in accuracy. This result shows BiGBERT is stable and have significantly improved on one-shot text classification.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126249536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Exploration of LoRa Network in Tropical Farming Environment 热带农业环境下LoRa网络的探索
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807765
Carlos Gonzalez, S. Gibeaux, D. Ponte, Asael Espinosa, Javier Pittí, F. Nolot
{"title":"An Exploration of LoRa Network in Tropical Farming Environment","authors":"Carlos Gonzalez, S. Gibeaux, D. Ponte, Asael Espinosa, Javier Pittí, F. Nolot","doi":"10.1109/CCAI55564.2022.9807765","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807765","url":null,"abstract":"Agricultural Internet of Things (IoT) applications represent a great help in collect environmental data and guide the farmer to make better decisions. Due to the data range of wireless networks and low power capabilities, one promising technologies for Low-Power-Wide-Area-Network (LPWAN) is LoRa systems. This study provides the performance evaluation of outdoor Lora signal propagation within the range 902-928 megahertz frequency in tropical environment. The behavior of LoRa communication in a tropical vegetation appeared smaller than the theoretical expected range. A dense vegetation intensely reduces the communication range impacting the spreading factor and coding rate. The sensor nodes are positioned from several hundred meters of a point collection in a rural environment. Different propagation models and checkpoint have been compared to the measurement metrics of the LoRa performance evaluation including the Signal to Noise Ratio (SNR), the Received Packet Ratio (RPR), Received Signal Strength Indication (RSSI).","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128402160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on the Influence of Catalyst Theory Based on Big Data on Urban Furniture Design 基于大数据的催化剂理论对城市家具设计的影响研究
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807760
Y. Tian, Hongyan Xu
{"title":"Research on the Influence of Catalyst Theory Based on Big Data on Urban Furniture Design","authors":"Y. Tian, Hongyan Xu","doi":"10.1109/CCAI55564.2022.9807760","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807760","url":null,"abstract":"This paper analyzes urban furniture design through big data, and explores the factors affecting urban furniture design combined with catalyst theory, so as to provide more diversified development ideas for the development of urban furniture design. Based on the theory of urban catalyst, this paper studies and analyzes the factors from three aspects: infrastructure construction, system planning and multi-party linkage, explores the factors affecting urban furniture design, and explores the feasibility and design strategy of urban furniture as urban catalyst, so as to give full play to the catalytic effect of urban furniture, enhance the vitality and value of urban public space, and then have a positive impact on urban construction.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129570489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Multi-Parameter Intelligent Communication Anti-Jamming Method Based on Three-Dimensional Q-Learning 基于三维q -学习的多参数智能通信抗干扰方法
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807745
Ziming Pu, Yingtao Niu, Guoliang Zhang
{"title":"A Multi-Parameter Intelligent Communication Anti-Jamming Method Based on Three-Dimensional Q-Learning","authors":"Ziming Pu, Yingtao Niu, Guoliang Zhang","doi":"10.1109/CCAI55564.2022.9807745","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807745","url":null,"abstract":"Aiming at the deficiency of the traditional intelligent communication anti-jamming algorithms, a multi-parameter intelligent anti-jamming method for wireless communication based on three-dimensional Q-learning (3DQL) is proposed. In the case of multi-channel random jamming, the wireless communication system guides the transmitter to choose the optimal channel and power for communication or keep silent in each timeslot by learning the random variation law of channel, timeslot and power of jamming, achieving the anti-jamming effect of optimal decision accuracy and timeslot utilization. Simulation results show that the 3DQL algorithm is better than the traditional two-dimensional Q-learning (2DQL) algorithm in decision accuracy and slot utilization.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"1963 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133872312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Collaborative Anti-jamming Algorithm Based on Q-learning in Wireless Communication Network 基于q -学习的无线通信网络协同抗干扰算法
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807740
Guoliang Zhang, Yonggui Li, Luliang Jia, Yingtao Niu, Quan Zhou, Ziming Pu
{"title":"Collaborative Anti-jamming Algorithm Based on Q-learning in Wireless Communication Network","authors":"Guoliang Zhang, Yonggui Li, Luliang Jia, Yingtao Niu, Quan Zhou, Ziming Pu","doi":"10.1109/CCAI55564.2022.9807740","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807740","url":null,"abstract":"Aiming at defending against the malicious jamming attacks and considering the interference among users in the multi-user wireless networks, a collaborative anti-jamming algorithm based on Q-learning in wireless communication network (CAAQ) is proposed in this paper. Specifically, since there exists the competition and collaboration among the users, the metric is first applied to determine whether there has interference among users by adding the distance threshold, which can significantly decrease both the training time and the complexity of multi-agent Reinforcement Learning (RL). Then, through the user-to-user collaboration at the information interaction level, a collaborative anti-jamming algorithm based on Q-learning is proposed to optimize the spectrum allocation for all users. Numerical results verify the superiority and substantive of the proposed CAAQ, which can simultaneously avoid the interference among the users and overcome the malicious jamming attack.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133902591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An NDN-Enabled Differentiated Routing Strategy for Blockchain 区块链使能ndn的差异化路由策略
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807743
Jiajia Shang, R. Huo, Shuo Wang, Tao Huang
{"title":"An NDN-Enabled Differentiated Routing Strategy for Blockchain","authors":"Jiajia Shang, R. Huo, Shuo Wang, Tao Huang","doi":"10.1109/CCAI55564.2022.9807743","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807743","url":null,"abstract":"Recently, blockchain has been used in financial payment, smart medicine, drones and other scenarios with its decentralized trust mechanism, distributed data storage, and non-tamper proof. Differentiated quality of service (QoS) assurance has become a measure to deal with the multi-directional expansion of blockchain and the wide range of application scenarios, but there are still problems that need to be solved in terms of providing differentiated QoS assurance. The blockchain provides two basic services, transactions and block synchronization. The QoS requirements of the application scenarios are not considered. This paper deploys the named data networking (NDN) at the network layer of the blockchain. In this paper, packets are shaped hop-by-hop at the router node. The window sliding strategy is used to establish the mapping relationship between packets and windows, so as to speed up the processing speed of packets and reduce the average delay of packet transmission. This paper also designs a forwarding strategy, which distributes packets to the forwarding queue according to the delay sensitivity of the packets in the sliding window and the possibility of packet timeout, so as to meet the delay requirements of delay-sensitive packets.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134643187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
RM-Transformer: A Transformer-based Model for Mandarin Speech Recognition RM-Transformer:一个基于transformer的普通话语音识别模型
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807706
Xingmin Lu, Jianguo Hu, Shenhao Li, Yanyu Ding
{"title":"RM-Transformer: A Transformer-based Model for Mandarin Speech Recognition","authors":"Xingmin Lu, Jianguo Hu, Shenhao Li, Yanyu Ding","doi":"10.1109/CCAI55564.2022.9807706","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807706","url":null,"abstract":"A network called RM-Transformer is proposed in this paper for Mandarin speech recognition. The proposed RMTransformer can make full use of features from different layers in the network instead of features solely from the top layer, which is used in the traditional models. Moreover, the proposed network has excellent capability in addressing the ambiguity problems caused by homophone phenomenon in Mandarin speech recognition task. Empirical evaluations have been conducted in two widely used datasets, which are Aishell-l and Aidatatang-200zh. Experimental results can verify the effectiveness of the proposed scheme.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132056315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
BERT-RF Fusion Model Based on Bayesian optimization for Sentiment Classification 基于贝叶斯优化的BERT-RF情感分类融合模型
2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807729
Ying-Chih Shen, Mincheng Chen, Siqi Cai, Shaojie Hu, Xing Chen
{"title":"BERT-RF Fusion Model Based on Bayesian optimization for Sentiment Classification","authors":"Ying-Chih Shen, Mincheng Chen, Siqi Cai, Shaojie Hu, Xing Chen","doi":"10.1109/CCAI55564.2022.9807729","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807729","url":null,"abstract":"Social media platforms have accumulated massive amounts of social text data. Mining people’s sentiment tendencies from these data is great significance. However, compared with ordinary text, social text data is shorter and more colloquial, and it is difficult to extract feature information, which affects the accuracy of sentiment classification. To improve the accuracy of sentiment classification, the BERT-RF fusion model based on Bayesian optimization for sentiment classification is proposed. Firstly, the key features in the social text are extracted through the deep structure of the BERT model, and the random forest model is used to replace the final output layer of the BERT, and the relevant social text is classified according to the key features. Hyperparameters of random forest are optimized using Bayesian method. The experimental results show that our model has better performance for sentiment classification.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133555506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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