2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)最新文献_第3页

A Novel Attention-guided Network for Deep High Dynamic Range Imaging 一种用于深度高动态范围成像的新型注意引导网络

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00069

Qinghan Jiang, Ying Huang, Su Liu, Zequan Wang, Tangsheng Li

{"title":"A Novel Attention-guided Network for Deep High Dynamic Range Imaging","authors":"Qinghan Jiang, Ying Huang, Su Liu, Zequan Wang, Tangsheng Li","doi":"10.1109/CTISC52352.2021.00069","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00069","url":null,"abstract":"In natural scenes with multi-exposure image fusion (MEF), high dynamic range (HDR) imaging is often affected by moving objects or misalignments in the scene, resulting in ghosting artifacts in the final imaging results, with the help of optical flow method and deep network architecture. To avoid ghosting artifacts better, we propose a novel attention- guided neural network (ADeepHDR) to produce high-quality ghost-free HDR images. Unlike the previous methods, we use the attention module to guide the process of image merging. The attention module can detect the large motions and the notable parts of the different input features and enhance details in the results. Based on the attention module, we also try different subnetwork variants to make full use of the hierarchical features to get more ideal results. Besides, fractional-oder differential convolution is used in the subnetwork variant to extract more detailed features. The proposed ADeepHDR is an improvement method without optical flows, which can better avoid the ghosting artifacts caused by error optical flow estimation and large motions. We have conducted extensive quantitative and qualitative assessments, and show that the proposed method is superior to the most state-of-the- art approaches.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117031489","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 adaptive-stylization-based dynamic residual-multi-stage and multi-pooling approach to arbitrary image style transfer 基于自适应风格的动态残差多阶段多池化任意图像风格转移方法

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00070

Wenrui Yi, Anmin Zhu

{"title":"An adaptive-stylization-based dynamic residual-multi-stage and multi-pooling approach to arbitrary image style transfer","authors":"Wenrui Yi, Anmin Zhu","doi":"10.1109/CTISC52352.2021.00070","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00070","url":null,"abstract":"Style transfer means that the characteristic information of the style image is transferred to the content image under a given content and style picture. Meanwhile, the transferred image is faithful to the content image. Currently, the transferred image has many problems such as artifacts and distortion of the spatial structure. To solve these problems, a dynamic residual-multi-level and multi-pooling network combined with our improved style transfer algorithm is proposed in this paper to achieve better effect of arbitrary image style transfer. To elaborate more specifically, First of all, We add a dynamic-residual network layer to the network to speed up the training speed of the network model and improve the robustness of the network. Secondly, we use a multi-level and multi-pooling layer to extract more specific spatial structure, edge texture and other information in the image, and the network layer also has an additional denoising effect. Third, the improved style transfer algorithm aligns the mean and variance of the content and style images from a statistical point of view, and realizes adaptive arbitrary style transfer of the original input image features. Finally, during model training, the convergence speed of the proposed approach is faster than other current advanced methods, and the transferred renderings are better than other network models in quantitative comparisons such as SSIM and Gram. It is worth mentioning that our model has the fastest real-time transfer speed and can realize any image style transfer.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114262927","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

Facial Feature Information Based Computer-aided Emoticon Design System 基于面部特征信息的计算机辅助表情符号设计系统

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00061

Chen Zhu, Wang Yan, Si Zheng Ming

引用次数: 0

An Algorithm Based on Attention Mask for Fine-grained Object Detection 一种基于注意掩模的细粒度目标检测算法

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00065

Ying Zhu, J. Zhuang, Jiangjian Xiao, Kangkang Song, Li Lv, Sisi Lao

{"title":"An Algorithm Based on Attention Mask for Fine-grained Object Detection","authors":"Ying Zhu, J. Zhuang, Jiangjian Xiao, Kangkang Song, Li Lv, Sisi Lao","doi":"10.1109/CTISC52352.2021.00065","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00065","url":null,"abstract":"The demands of the application of deep learning for the detection and marking of fine-grained object in the large field of view are increasingly prominent, which can be seen from automatic driving, traffic sign detection, satellite image analysis and so on. Most of the current studies focusing on the fine-grained object detection make an improvement based on the existing object detection algorithms to increase the detection accuracy of fine-grained object. This paper will propose a novel algorithm based on neural network feature constraints, which can realize the detection and marking of fine-grained object via network with the guidance of an attention map. In the procedures of neural network training, the Attention Mask is employed to constrain the loss function of the network and extract feature maps of key areas to alter the weights of key features through self-adaption. In this paper, combined with the needs of nematode detection project, taking nematode detection as an example, the ablation experiments with the employment of UNet network demonstrate that the accuracy rate of fine-grained object detection is increased from zero to around 85% with the additional loss function constraints.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124866280","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

Machine learning product key performance indicators and alignment to model evaluation 机器学习产品关键性能指标与模型评估的一致性

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00039

Ioannis Bakagiannis, V. Gerogiannis, George Kakarontzas, A. Karageorgos

引用次数: 0

Predicting the Association Between Human Drugs and Targets based on HeteSim Score 基于HeteSim评分预测人类药物与靶标之间的关联

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00012

Le Wei, Fang Zheng

{"title":"Predicting the Association Between Human Drugs and Targets based on HeteSim Score","authors":"Le Wei, Fang Zheng","doi":"10.1109/CTISC52352.2021.00012","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00012","url":null,"abstract":"In the past decades, drug target prediction has attracted a lot of scholars' attention, and many classic algorithms and models have also been generated. We study from machine learning algorithms and construct a heterogeneous network of drug targets through data processing of biological networks. We construct a drug-drug-like network, a drug-target similar network, and a target-target similar network, and then integrate the above three networks into one heterogeneous network, and select two meta-paths in the heterogeneous network \"drug-drug-target\" path and the \"drug-target-target\" path, the normalized HeteSim scores for both paths were calculated, and the HeteSim scores for the two paths were integrated to obtain the final result. The drug-target interaction score (HDTA_HeteSim) is calculated based on different paths in heterogeneous networks, and the algorithm is applied to drug and target prediction. In addition, the AUC (area under the ROC curve) of the HDTA_HeteSim model in the leave-one-out cross-validation has a value of 0.9540, which achieves reliable prediction performance. We also used the DT-Hybrid method and the HDTA_HeteSim method to separately analyze the bromocriptine drug predictions, and found that our algorithm are more efficiency.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121469970","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

LGANet: Local Graph Attention Network for Peer-to-Peer Botnet Detection LGANet:用于点对点僵尸网络检测的局部图关注网络

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00013

Yunyi Yang, Liming Wang

{"title":"LGANet: Local Graph Attention Network for Peer-to-Peer Botnet Detection","authors":"Yunyi Yang, Liming Wang","doi":"10.1109/CTISC52352.2021.00013","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00013","url":null,"abstract":"Botnets have become one of significant intrusion threats against network security. The decentralized nature of Peer-to-Peer (P2P) botnets makes them easy to survive and hard to be detected. In this paper, we propose Local Graph Attention Network (LGANet), a novel framework that detects P2P bots precisely utilizing both network traffic-based features and topological features. Firstly, we consider each node in the network communication graph as a centroid and construct a local graph for generating contextual-aware features. Secondly, the local graph attention mechanism is applied to the local graph aiming to pay attention to most topology-relative information. Moreover, to fully capture various features in different representation sub-spaces, a multi-head local graph attention layer is constructed which contains multiple single-head local graph attention layers in parallel. Thirdly, we design an adaptive gate fusion module which fuses features in different levels adaptively and produces an enriched presentation. Extensive experimental results demonstrate the effectiveness of our LGANet for P2P botnet detection.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132932475","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: 3

Inter-satellite token ring Ad hoc network technology for micro-nano satellite cluster collaboration 面向微纳卫星集群协作的星间令牌环自组网技术

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00031

Jianyun Chen, Sili Liu, Yonggang Zhang

{"title":"Inter-satellite token ring Ad hoc network technology for micro-nano satellite cluster collaboration","authors":"Jianyun Chen, Sili Liu, Yonggang Zhang","doi":"10.1109/CTISC52352.2021.00031","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00031","url":null,"abstract":"With the rapid increase of satellite formation network demand, inter-satellite link technology has become a research hot-spot issue of space-based Ad hoc network. Token ring protocol is a mature networking protocol for LAN, which has been widely used in Ad hoc networks. As a non-compete protocol, it can effectively solve the problem of channel access control and avoid the problems of hidden and exposed nodes in wireless communication. Based on token ring protocol, we proposed an inter-satellite token ring Ad hoc protocol, and its state transfer mechanism, network access process, leaving network process, and frame type are discussed in detail. We determined the hardware which is composed of SoC platform of Zyqn7000 series and AD9361, and conducted relevant performance analysis in combination with the specific micro-nano satellite cluster geometry. By using the inter-satellite wireless self-organizing token ring protocol, the networking time of the 15 satellites in this cluster is less than 10s, and the end-to-end delay of the satellite network is controlled in seconds. In addition, the protocol supports new nodes to enter the network and fault nodes to leave the network, which greatly improves the network flexibility.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114993786","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: 3

A new unsupervised Algorithm for extracting relationship words between two entities 一种新的实体间关系词的无监督提取算法

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00037

Fan Wu, Taihao Zheng, L. Yao, Honghai Feng

{"title":"A new unsupervised Algorithm for extracting relationship words between two entities","authors":"Fan Wu, Taihao Zheng, L. Yao, Honghai Feng","doi":"10.1109/CTISC52352.2021.00037","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00037","url":null,"abstract":"Purpose: In order to use a popular supervised learning algorithm such as BERT to extract the relationships of concepts (triple relationship extraction), it is necessary to label the relationship types manually. If some relation words are not been labeled in the training stag, they cannot be recognized probably in the test stage and the corresponding entities cannot been recognized accordingly. This paper proposes a new unsupervised algorithm to extract as many relation words as possible of two entities, especially those that are easily overlooked. Methods: The disease-cause relationship was taken as an example, and 10204 effective sentences of disease and corresponding causes were extracted by web crawler. According to the constraints of syntactic, semantic and lexical features, the relationship words were extracted with an unsupervised manner, and the automatic extracted results were summarized. Results: Some specific relation words that are ignored in manual labeling stage are found; the conjoining relation words often appeared together in the texts are recognized; some types and features of relation words are obtained. These types and features can be used to help the relation labeling in the supervised learning stage, and to help expanding the relevant knowledge graphs and improving the accuracy of information retrieval.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126148452","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

MDACD: A Model-Driven Development Method for Avionics Software Components 航电软件组件模型驱动开发方法

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00028

Chunling Sheng, Pei Hong, Zhen Wang, S. Liu, Baibing Cao, Jiexiang Kang

引用次数: 0