2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)最新文献

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Reference-frame-independent measurement device-independent quantum key distribution with heralded single-photon sources 与参考帧无关的测量设备与预示的单光子源无关的量子密钥分配
Jun Lu, Wenhe Zhuo, Liangjie Cui, Zhenwei Li, Junjie Ma, Yuxiang Bian, Chunhui Zhang
{"title":"Reference-frame-independent measurement device-independent quantum key distribution with heralded single-photon sources","authors":"Jun Lu, Wenhe Zhuo, Liangjie Cui, Zhenwei Li, Junjie Ma, Yuxiang Bian, Chunhui Zhang","doi":"10.1109/ICECAI58670.2023.10177006","DOIUrl":"https://doi.org/10.1109/ICECAI58670.2023.10177006","url":null,"abstract":"Quantum cryptography has a strong security that classical cryptography cannot match, and quantum key distribution (QKD) is the most important method of quantum cryptography. Reference-frame-independent measurement-device-independent QKD (RFI-MDI-QKD) protocol can avoid the drift of the reference frame and all side channels of measurement devices. However, RFI MDI-QKD usually employs a weak coherent state (WCS) as the light source, which limits the transmission distance of secure keys. Here, by using heralded single-photon sources, we investigate an improved decoy-state RFI-MDI-QKD scheme. We perform numerical simulations for the scheme under different scenarios, such as considering the finite size and different drift angles. Compared with the scheme with WCS, our scheme can significantly improve the transmission distance of RFI-MDI-QKD.","PeriodicalId":189631,"journal":{"name":"2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127195867","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
ICECAI 2023 Cover Page ICECAI 2023封面
{"title":"ICECAI 2023 Cover Page","authors":"","doi":"10.1109/icecai58670.2023.10177000","DOIUrl":"https://doi.org/10.1109/icecai58670.2023.10177000","url":null,"abstract":"","PeriodicalId":189631,"journal":{"name":"2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132704848","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
Shared Bike Demand Prediction Based on Combined Deep Learnings 基于联合深度学习的共享单车需求预测
Chuanxiang Ren, Hui Xu, Chunxu Chai, Fangfang Fu
{"title":"Shared Bike Demand Prediction Based on Combined Deep Learnings","authors":"Chuanxiang Ren, Hui Xu, Chunxu Chai, Fangfang Fu","doi":"10.1109/ICECAI58670.2023.10176751","DOIUrl":"https://doi.org/10.1109/ICECAI58670.2023.10176751","url":null,"abstract":"The shared bike demand prediction can support shared bike scheduling activities and provide more convenient services for users. In this paper, a combined deep learning model, i.e., CNN-GRU-Attention model, is established. The model uses CNN network to extract local features of shared bike demand, GRU network to make predictions, and attention mechanism to extract important features. The parameters such as the number of neurons in the model are set experimentally. The simulation results show that the model has higher accuracy compared with other baseline models. It can fit the demand trend of shared bikes well and has good performance.","PeriodicalId":189631,"journal":{"name":"2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117320394","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 Green Energy Efficient D2D Cooperative Communication 一种绿色节能D2D协同通信技术
Yang Yu, Jiangchen Zhang, Hongping Lin, Quanwen Fang
{"title":"A Green Energy Efficient D2D Cooperative Communication","authors":"Yang Yu, Jiangchen Zhang, Hongping Lin, Quanwen Fang","doi":"10.1109/ICECAI58670.2023.10176468","DOIUrl":"https://doi.org/10.1109/ICECAI58670.2023.10176468","url":null,"abstract":"Green communication is an important development trend in the wireless communication industry. Its main purpose is to minimize the energy consumption of communication systems while ensuring the quality of service for the users. For wireless communication networks, especially in today’s large-scale commercialization of 5G, optimizing the battery consumption of mobile phone terminals and extending the network lifetime is the focus of green communication. Based on green communication, this paper proposes a wireless resource allocation scheme in a D2D cooperative communication scenario. This scheme can not only assist cell users in completing data transmission but also provide transmission opportunities for D2D terminals. It can significantly reduce terminal energy consumption and extend network lifetime.","PeriodicalId":189631,"journal":{"name":"2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132116666","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 review of visible single target tracking based on Siamese networks 基于暹罗网络的可见单目标跟踪研究综述
Wenji Yin, Zecong Ye, Yueping Peng, Wenchao Liu
{"title":"A review of visible single target tracking based on Siamese networks","authors":"Wenji Yin, Zecong Ye, Yueping Peng, Wenchao Liu","doi":"10.1109/ICECAI58670.2023.10176667","DOIUrl":"https://doi.org/10.1109/ICECAI58670.2023.10176667","url":null,"abstract":"Visible single-target tracking is an important research direction in computer vision. Recently, twin networks have shown strong performance in target tracking. Although there have been many single-target tracking reviews, there are few summaries of single-target twin networks. In order to better clarify the development status of twin networks, 17 popular trackers are divided into five categories, and the performance of these trackers in five data sets, such as LASOT, OTB and VOT, is discussed and analyzed—finally, the development of single target tracking in twin network prospects.","PeriodicalId":189631,"journal":{"name":"2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127921596","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
Relationship between the Difficulty of Words and its Distribution of Numbers of Tries in Wordle 单词难度与单词在世界范围内尝试次数分布的关系
Junjie Yang, Xueji Fang, Qiyuan Pei
{"title":"Relationship between the Difficulty of Words and its Distribution of Numbers of Tries in Wordle","authors":"Junjie Yang, Xueji Fang, Qiyuan Pei","doi":"10.1109/ICECAI58670.2023.10176549","DOIUrl":"https://doi.org/10.1109/ICECAI58670.2023.10176549","url":null,"abstract":"In this paper, we aim to explore the relationship between the difficulty of words and the distribution of the number of tries (DNT) in Wordle, a popular word-guessing puzzle game around the world. To do so, we first define and extract six attributes of words, namely Information Entropy (IE), Word Frequency (WF), Letter-Pair Frequency (LPF), Letter Frequency (LF), Vowel Rate (VR), and Repeat Letter Rate (RLR), and use Principal Component Analysis (PCA) to assess the redundancy of these features. Then, we employ a self-supervised learning method to train an Autoencoder (AE) for learning the DNT features, with a 3-dimensional latent variable in the bottleneck section. Subsequently, we use this latent variable as a supervised label and the word attributes as input to train a Multilayer Perceptron (MLP) to approximate the latent variable. Finally, the MLP-AE Fusion Model can predict the DNT for an arbitrary given word. Based on experimental results, our model achieves an MAE of 3.53 and a JS divergence of 99.45% in tests, highlighting the significance of our work in offering an effective prediction approach for the correlation between word difficulty and the distribution of the number of attempts.","PeriodicalId":189631,"journal":{"name":"2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127974165","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
Semantic-Selection-Strategy: A Transfer Reinforcement Learning Framework for Visual Interference 语义-选择-策略:视觉干扰的迁移强化学习框架
Yanyou Lv, Zhongjian Yang
{"title":"Semantic-Selection-Strategy: A Transfer Reinforcement Learning Framework for Visual Interference","authors":"Yanyou Lv, Zhongjian Yang","doi":"10.1109/ICECAI58670.2023.10176802","DOIUrl":"https://doi.org/10.1109/ICECAI58670.2023.10176802","url":null,"abstract":"Despite the significant achievements of deep reinforcement learning in a wide range of domains, there is still the generalization problem. Traditional transfer methods cannot adapt quickly in the face of large-scale visual disturbances. To improve this transfer performance, this paper introduces semantic segmentation, a hard attention mechanism, and first proposes a framework of semantic selection strategy, which achieves transfer process through three parts: semantic segmentation, weight selection, and strategy inheritance. Through experiments, it is verified that this method has certain advantages in comprehensive evaluation indicators especially in the jumpstart compared with other methods.","PeriodicalId":189631,"journal":{"name":"2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125185237","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 New Strategy for Improving the Accuracy in Scene Text Recognition 一种提高场景文本识别准确率的新策略
Fang Zheng, Chen Chen, Kai Wang, W. Wang
{"title":"A New Strategy for Improving the Accuracy in Scene Text Recognition","authors":"Fang Zheng, Chen Chen, Kai Wang, W. Wang","doi":"10.1109/ICECAI58670.2023.10176817","DOIUrl":"https://doi.org/10.1109/ICECAI58670.2023.10176817","url":null,"abstract":"Scene text recognition is the process of recognizing and converting text in natural images, such as street signs or billboards, into machine-readable text. However, there are some complexities, background variability, and even serious interference, and the text may also have diversity, which can lead to errors in recognition. This paper proposes an optimized text recognition strategy that can reduce errors by combining convolutional neural networks and recurrent neural networks to achieve accurate recognition of text in text images. A sequence learning model based on recurrent neural networks was used to process text sequences of different lengths, and CTC Loss was used to train the model. In addition, we also used data augmentation methods to increase the diversity of training data, in order to improve the robustness and generalization ability of the model. We conducted experiments on multiple public datasets to validate the performance and effectiveness of our proposed model. The experimental results showed that our method has reached the level of state-of the-art methods in terms of accuracy in detection results.","PeriodicalId":189631,"journal":{"name":"2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123670666","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
Air Quality Prediction Based on Graph Attention Network 基于图关注网络的空气质量预测
Chuhan Deng, Lanyu Liu, Chao Wang, Zhuo Chen,
{"title":"Air Quality Prediction Based on Graph Attention Network","authors":"Chuhan Deng, Lanyu Liu, Chao Wang, Zhuo Chen,","doi":"10.1109/ICECAI58670.2023.10176911","DOIUrl":"https://doi.org/10.1109/ICECAI58670.2023.10176911","url":null,"abstract":"Air quality is related to air pollution, weather, and exhaust emissions. Several previous studies have used temporal data for air quality prediction. However, the air is diffuse, and the air between sites will affect each other. Therefore, we found that more than temporal data is needed to predict air quality accurately. Spatial data between sites should also be considered. An air quality prediction model based on graph attention network is proposed in this paper. The spatial data are used for the graph attention network to obtain the influence of adjacent sites. The spatial data between non-adjacent sites is obtained by diffusion convolution. The prediction is then completed by the gate recurrent unit. Experiments show that the model fully uses spatial data and accurately predicts air quality.","PeriodicalId":189631,"journal":{"name":"2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122145143","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
Adaptive Lazily Aggregation Based on Error Accumulation 基于错误累积的自适应惰性聚合
Xiaofeng Chen, Gang Liu
{"title":"Adaptive Lazily Aggregation Based on Error Accumulation","authors":"Xiaofeng Chen, Gang Liu","doi":"10.1109/ICECAI58670.2023.10176452","DOIUrl":"https://doi.org/10.1109/ICECAI58670.2023.10176452","url":null,"abstract":"Federated Learning (FL) enables multiple clients to collaboratively train models without exposing their local data. FL is an effective approach to utilizing localized data while preserving clients’ data privacy, but it also brings significant communication overhead. To reduce communication overhead of FL, this paper proposes the Adaptive Lazily Aggregation based on Error Accumulation (EA-ALA) algorithm. It uses adaptive constraints to determine whether a client can skip a communication round with the server so as to diminish communication cost. It also adopts error accumulation to improve model accuracy. The experimental results on CIFAR10 and Fashion-MNIST datasets show that compared to vanilla FL, EA-ALA consumes only 52% and 61% of communication rounds to achieve higher model accuracy.","PeriodicalId":189631,"journal":{"name":"2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126249109","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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