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2021 International Conference on Digital Society and Intelligent Systems (DSInS)最新文献

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Fast and Dense Denoising Convolutional Neural Network 快速密集去噪卷积神经网络
2021 International Conference on Digital Society and Intelligent Systems (DSInS) Pub Date : 2021-12-03 DOI: 10.1109/dsins54396.2021.9670615
Y. Zeng, Tengfei Liang, Yi Jin, Yidong Li, Zhigang Wang
{"title":"Fast and Dense Denoising Convolutional Neural Network","authors":"Y. Zeng, Tengfei Liang, Yi Jin, Yidong Li, Zhigang Wang","doi":"10.1109/dsins54396.2021.9670615","DOIUrl":"https://doi.org/10.1109/dsins54396.2021.9670615","url":null,"abstract":"Deep neural networks show us their superior image denoising capability due to the powerful fitting ability. However, they suffer from the following drawbacks: (i) too deep neural networks often imply a very large number of parameters and considerable computational overhead; (ii) neural networks that are too deep are difficult to converge by training and may lead to degradation. In this study, we propose a novel denoising network called the fast and dense denoising convolutional neural network(FDDCNN). In particular, the depthwise separable convolutions in the fast module and the homogeneous cascade structure in the dense module can efficiently solve the above problem. Extensive experiments with publicly available datasets have shown that this model can have the same excellent denoising power as existing methods with fewer parameters and less computational overhead.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127787389","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
Sign Language Recognition and Translation Method based on VTN 基于VTN的手语识别与翻译方法
2021 International Conference on Digital Society and Intelligent Systems (DSInS) Pub Date : 2021-12-03 DOI: 10.1109/dsins54396.2021.9670588
Wu Qin, Xue Mei, Yuming Chen, Qihang Zhang, Yanyin Yao, S. Hu
{"title":"Sign Language Recognition and Translation Method based on VTN","authors":"Wu Qin, Xue Mei, Yuming Chen, Qihang Zhang, Yanyin Yao, S. Hu","doi":"10.1109/dsins54396.2021.9670588","DOIUrl":"https://doi.org/10.1109/dsins54396.2021.9670588","url":null,"abstract":"Sign language recognition plays an important role in real-time sign language translation, communication for deaf people, education and human-computer interaction. However, vision-based sign language recognition faces difficulties such as insufficient data, huge network models and poor timeliness. We use VTN (Video Transformer Net) to construct a lightweight sign language translation network. We construct the dataset called CSL_BS (Chinese Sign Language-Bank and Station) and two-way VTN to train isolated sign language and compares it with I3D (Inflated three Dimension). Then I3D and VTN are respectively used as feature extraction modules to extract the features of continuous sign language sequences, which are used as the input of the continuous sign language translation decoding network (seq2seq). Based on CSL-BS, two-way VTN achieves 87.9% accuracy while two-way I3D is 84.2%. And the recognition speed is increased by 46.8%. In respect of continuous sign language translation, the accuracy of VTN_seq2seq is 73.5% while I3D_seq2seq is 71.2%, the recognition speed is 13.91s and 26.54s respectively.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117239244","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: 8
Brainstorming Multi-Agent Reinforcement Learning for Multi-Vehicles Games 多车辆游戏的多智能体强化学习集思广益
2021 International Conference on Digital Society and Intelligent Systems (DSInS) Pub Date : 2021-12-03 DOI: 10.1109/dsins54396.2021.9670611
Yingxiang Liu, Hao Li, Xuefeng Zhu
{"title":"Brainstorming Multi-Agent Reinforcement Learning for Multi-Vehicles Games","authors":"Yingxiang Liu, Hao Li, Xuefeng Zhu","doi":"10.1109/dsins54396.2021.9670611","DOIUrl":"https://doi.org/10.1109/dsins54396.2021.9670611","url":null,"abstract":"Every year, traffic accidents in the world cause economic losses equivalent to 600 billion dollars. And autonomous driving technology can improve driving safety and traffic efficiency. Therefore, unmanned vehicles are the development direction of future transportation, and decision-making control is an important issue that needs to be faced in the development of unmanned driving technology. The existing reinforcement learning algorithms are mostly limited to the research of single agent. Combining the reality of multi-vehicles driving on the road at the same time, in this work, we propose brainstorming multiagent reinforcement learning (BMARL) to guide the decision-making of multi-vehicles autonomous driving. The basic framework of BMARL is based on the actor-critic network structure. It adopts a centralized training and decentralized execution structure. A Critic network and two Actor networks are trained for each agent. This article uses the intelligent driving simulation environment commonly used in the field of artificial intelligence, and the open source racing simulator (TORCS) simulates the algorithm to verify the effectiveness of the above algorithm in the field of automatic driving decision-making control.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"254 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114008799","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
Learning Personalized End-to-End Task-Oriented Dialogue for Fast and Reliable Adaptation 学习以任务为导向的个性化端到端对话，实现快速可靠的自适应
2021 International Conference on Digital Society and Intelligent Systems (DSInS) Pub Date : 2021-12-03 DOI: 10.1109/dsins54396.2021.9670559
Shuang Qiu, Kang Zhang
{"title":"Learning Personalized End-to-End Task-Oriented Dialogue for Fast and Reliable Adaptation","authors":"Shuang Qiu, Kang Zhang","doi":"10.1109/dsins54396.2021.9670559","DOIUrl":"https://doi.org/10.1109/dsins54396.2021.9670559","url":null,"abstract":"Personalized task-oriented dialog agents can select responses according to the personalities of users. In this way, they facilitate understanding, and improve the efficiency of the conversation. Existing personalized agents generate matching functions according to human designed persona descriptions. This agent works well in the traditional scenario where sufficient samples are used, but can hardly fast adapt to new personas with few samples. In this paper, we propose to extend meta-learning algorithms to personalized end-to-end task-oriented dialogue learning, and train a switch model to allow for human agent use. Our model learns to quickly adapt to new personas leveraging a few dialogue samples and requesting human agents, while maximizing the task success of users. Empirical results on the Personalized bAbI dataset indicate that our proposal is effective in achieving the desired goals.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127954469","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: 2
Query-Adaptive Feature Fusion Base on Convolutional Neural Networks for Remote Sensing Image Retrieval 基于卷积神经网络的查询自适应特征融合遥感图像检索
2021 International Conference on Digital Society and Intelligent Systems (DSInS) Pub Date : 2021-12-03 DOI: 10.1109/dsins54396.2021.9670607
Famao Ye, Shuxiu Chen, Xianglong Meng, Junwei Xin
{"title":"Query-Adaptive Feature Fusion Base on Convolutional Neural Networks for Remote Sensing Image Retrieval","authors":"Famao Ye, Shuxiu Chen, Xianglong Meng, Junwei Xin","doi":"10.1109/dsins54396.2021.9670607","DOIUrl":"https://doi.org/10.1109/dsins54396.2021.9670607","url":null,"abstract":"Content-based Remote sensing image retrieval (CBRSIR) becomes important research with the volume of remote sensing images rapidly expanding. Many image features have been proposed for CBRSIR, hence it has become a big challenge to effectively fuse these features for alleviating the huge variation in retrieval performance among different image queries when a single image feature is used. We proposed a query-adaptive feature fusion method based on a convolutional neural networks (CNN) regression model. We use the CNN regression model to estimate the DCG value for each feature and assign different features with different weights for each query according to these DCG values. Meanwhile, we use the image-to-query-class distance to further improve retrieval performance. Experiments on UCMD show that the proposed method can improve the CBRSIR performance.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115974666","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
Rotation Transformation: A Method to Improve the Transferability of Adversarial Examples 旋转变换:一种提高对抗性示例可转移性的方法
2021 International Conference on Digital Society and Intelligent Systems (DSInS) Pub Date : 2021-12-03 DOI: 10.1109/dsins54396.2021.9670580
Zheming Li, Hengwei Zhang, Junqiang Ma, Bo Yang, Chenwei Li, Jingwen Li
{"title":"Rotation Transformation: A Method to Improve the Transferability of Adversarial Examples","authors":"Zheming Li, Hengwei Zhang, Junqiang Ma, Bo Yang, Chenwei Li, Jingwen Li","doi":"10.1109/dsins54396.2021.9670580","DOIUrl":"https://doi.org/10.1109/dsins54396.2021.9670580","url":null,"abstract":"Convolutional neural network models are fragile to adversarial examples. Adding disturbances that humans cannot observe in clean images can make the model classification error. Among the adversarial attack methods, white-box attacks have achieved a high attack success rate, but the \"overfitting\" between the adversarial examples and the model has led to a low success rate of black-box attacks. To this end, this paper introduces the data augmentation method into the adversarial examples generation process, establishes a probability model to perform random rotation transformation on clean images, improves the mobility of adversarial examples, and improves the success rate of adversarial examples under black-box setting. The experimental results on ImageNet show that the RO-MI-FGSM method we proposed has a stronger attack effect, achieving a black-box attack success rate up to 80.3%.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126912055","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
Masked Face Recognition based on Attention Mechanism and FaceX-Zoo 基于注意机制和FaceX-Zoo的蒙面人脸识别
2021 International Conference on Digital Society and Intelligent Systems (DSInS) Pub Date : 2021-12-03 DOI: 10.1109/dsins54396.2021.9670575
Yang Liu, Wenbin Zheng
{"title":"Masked Face Recognition based on Attention Mechanism and FaceX-Zoo","authors":"Yang Liu, Wenbin Zheng","doi":"10.1109/dsins54396.2021.9670575","DOIUrl":"https://doi.org/10.1109/dsins54396.2021.9670575","url":null,"abstract":"Recently, due to the outbreak of the COVID-19 epidemic in the world, wearing face masks has become a trend, which brings difficulties to the traditional face recognition technologies that do not actively focus on the upper part of the face. This paper proposes a novel method for masked face recognition based on attention mechanism and FaceX-Zoo (an open-source method of JD.COM). In order to make the module focus on the regions around the eyes, we integrated the CBAM (Convolutional Block Attention Module) attention mechanism into ResNet50 and MobileFaceNet network. Furthermore, the FaceX-Zoo method was used to generate masked face images to improve the module performance. Experiment results show that the proposed approach can improve the performance of masked face recognition compared with competitive approaches.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126106275","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
Characterizing Decades of Technological Advances with Graph Neural Networks: An Innovation Network Perspective 用图神经网络表征几十年的技术进步:一个创新网络的视角
2021 International Conference on Digital Society and Intelligent Systems (DSInS) Pub Date : 2021-12-03 DOI: 10.1109/dsins54396.2021.9670622
Yafei Jiang
{"title":"Characterizing Decades of Technological Advances with Graph Neural Networks: An Innovation Network Perspective","authors":"Yafei Jiang","doi":"10.1109/dsins54396.2021.9670622","DOIUrl":"https://doi.org/10.1109/dsins54396.2021.9670622","url":null,"abstract":"We leverage the detailed filing information of nearly seven million patents issued by the United States Patent and Trademark Office (USPTO) from 1975 to 2020 and construct a large-scale innovation network based on the forward and backward citations between patents. We employ several state-of-the-art graph neural network algorithms (Node2Vec, Attri2Vec, and GraphSAGE) to extract meaningful representations (embeddings) from patents by taking into account of the innovation network structure. Then, patents are clustered into groups of patents with a strong profile and structural similarity. In conclusion, the predictive power of patents’ learned embeddings demonstrates its usefulness for characterizing the innovation growth over the decades. These embeddings can effectively predict potential linkages between patents that provide a path for future innovation.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128191320","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
Weather classification method based on spiking neural network 基于峰值神经网络的天气分类方法
2021 International Conference on Digital Society and Intelligent Systems (DSInS) Pub Date : 2021-12-03 DOI: 10.1109/dsins54396.2021.9670557
Meng Tian, Xuefei Chen, Hongkuo Zhang, Peng Zhang, Kejing Cao, Ruiyi Wang
{"title":"Weather classification method based on spiking neural network","authors":"Meng Tian, Xuefei Chen, Hongkuo Zhang, Peng Zhang, Kejing Cao, Ruiyi Wang","doi":"10.1109/dsins54396.2021.9670557","DOIUrl":"https://doi.org/10.1109/dsins54396.2021.9670557","url":null,"abstract":"People's life and production activities are directly or indirectly affected by the weather. It is very necessary to accurately and quickly predict weather conditions. At present, the weather prediction system needs a series of sensors and manual assistance, but it cannot be arranged in high density due to high cost, which leads to inaccurate weather prediction. Computer vision technology can classify weather conditions through images, which reduces the cost and can be arranged in high density to ensure the accuracy of weather prediction. Because the training and reasoning of traditional p Convolutional Neural Network has very large energy consumption, while Spiking Neural Network has the characteristics of ultra-low energy consumption, which can further reduce the energy cost. In this paper, a shallow Spiking Neural Network for weather classification is constructed, which is trained and tested on a dataset containing four categories (cloudy, rainy, sunny and sunrise). Experiments show that the classification accuracy of the model is 93.45%, which is higher than that of the Convolutional Neural Network based on vgg19. In addition, the computational complexity of Spiking Neural Network and Convolutional Neural Network are analyzed to show the advantages of Spiking Neural Network in energy consumption.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125980911","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: 2
Electromagnetic mechanism property analysis for spacecraft despinning 航天器吊臂电磁机构特性分析
2021 International Conference on Digital Society and Intelligent Systems (DSInS) Pub Date : 2021-12-03 DOI: 10.1109/dsins54396.2021.9670567
Hao-kui Zhu, Yuan‐wen Zhang, Huan Huang
{"title":"Electromagnetic mechanism property analysis for spacecraft despinning","authors":"Hao-kui Zhu, Yuan‐wen Zhang, Huan Huang","doi":"10.1109/dsins54396.2021.9670567","DOIUrl":"https://doi.org/10.1109/dsins54396.2021.9670567","url":null,"abstract":"In this paper, the performance analysis of the electromagnetic vortex despinning mechanism based on Halbach configuration is carried out, and the ground test and verification system is designed. First, the main components of the system are introduced: the electromagnetic despinning mechanism and the controllable high-precision and high-speed rotation system. The controllable high precision and high speed rotating system is composed of rotating body, vacuum pump and upper mechanism. Then, different control group and experimental group are designed to conduct ground despinning test with the system, and the actual despinning effect of the designed electromagnetic despinning mechanism is analyzed. Finally, based on the experimental results, the improvement design and optimization direction of the electromagnetic despinning mechanism are proposed.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126702098","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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