Connection Science最新文献_第7页

CFSE: a Chinese short text classification method based on character frequency sub-word enhancement 基于字符频率子词增强的中文短文本分类方法

4区计算机科学

Connection Science Pub Date : 2023-10-06 DOI: 10.1080/09540091.2023.2263663

Xingguang Wang, Shunxiang Zhang, Zichen Ma, Yunduo Liu, Youqiang Zhang

{"title":"CFSE: a Chinese short text classification method based on character frequency sub-word enhancement","authors":"Xingguang Wang, Shunxiang Zhang, Zichen Ma, Yunduo Liu, Youqiang Zhang","doi":"10.1080/09540091.2023.2263663","DOIUrl":"https://doi.org/10.1080/09540091.2023.2263663","url":null,"abstract":"As a foundation task of natural language processing, text classification is widely used in information retrieval, public opinion analysis, and other related tasks. Facing the problem of sparse features of Chinese short texts, which affects the classification accuracy of Chinese short texts, this paper proposes a Chinese short text classification method based on the Character Frequency Sub-word Enhancement (CFSE), which can effectively improve the classification accuracy of Chinese short texts. First, the initial Chinese-character sequence is mapped to the corresponding Character Frequency Sub-word (CFS) sequence based on the global character1 frequency information. Second, the relationship features among data are extracted based on BiLSTM-Att processing CFS sequence, and the semantic features of the initial Chinese-character sequence are obtained through ERNIE. Finally, these two kinds of features are fused and input into the text classifier to obtain the classification results. Experimental results show that the proposed method can improve the classification accuracy of Chinese short texts.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135352481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention NAS-YOLOX:一种基于神经结构搜索和多尺度关注的SAR舰船检测方法

4区计算机科学

Connection Science Pub Date : 2023-10-04 DOI: 10.1080/09540091.2023.2257399

Hao Wang, Dezhi Han, Mingming Cui, Chongqing Chen

{"title":"NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention","authors":"Hao Wang, Dezhi Han, Mingming Cui, Chongqing Chen","doi":"10.1080/09540091.2023.2257399","DOIUrl":"https://doi.org/10.1080/09540091.2023.2257399","url":null,"abstract":"Due to the advantages of all-weather capability and high resolution, synthetic aperture radar (SAR) image ship detection has been widely applied in the military, civilian, and other domains. However, SAR-based ship detection suffers from limitations such as strong scattering of targets, multiple scales, and background interference, leading to low detection accuracy. To address these limitations, this paper presents a novel SAR ship detection method, NAS-YOLOX, which leverages the efficient feature fusion of the neural architecture search feature pyramid network (NAS-FPN) and the effective feature extraction of the multi-scale attention mechanism. Specifically, NAS-FPN replaces the PAFPN in the baseline YOLOX, greatly enhances the fusion performance of the model’s multi-scale feature information, and a dilated convolution feature enhancement module (DFEM) is designed and integrated into the backbone network to improve the network’s receptive field and target information extraction capabilities. Furthermore, a multi-scale channel-spatial attention (MCSA) mechanism is conceptualised to enhance focus on target regions, improve small-scale target detection, and adapt to multi-scale targets. Additionally, extensive experiments conducted on benchmark datasets, HRSID and SSDD, demonstrate that NAS-YOLOX achieves comparable or superior performance compared to other state-of-the-art ship detection models and reaches best accuracies of 91.1% and 97.2% on AP0.5, respectively.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"303 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual conditional GAN based on external attention for semantic image synthesis 基于外部注意的双条件GAN语义图像合成

4区计算机科学

Connection Science Pub Date : 2023-10-04 DOI: 10.1080/09540091.2023.2259120

Gang Liu, Qijun Zhou, Xiaoxiao Xie, Qingchen Yu

{"title":"Dual conditional GAN based on external attention for semantic image synthesis","authors":"Gang Liu, Qijun Zhou, Xiaoxiao Xie, Qingchen Yu","doi":"10.1080/09540091.2023.2259120","DOIUrl":"https://doi.org/10.1080/09540091.2023.2259120","url":null,"abstract":"Although the existing semantic image synthesis methods based on generative adversarial networks (GANs) have achieved great success, the quality of the generated images still cannot achieve satisfactory results. This is mainly caused by two reasons. One reason is that the information in the semantic layout is sparse. Another reason is that a single constraint cannot effectively control the position relationship between objects in the generated image. To address the above problems, we propose a dual-conditional GAN with based on an external attention for semantic image synthesis (DCSIS). In DCSIS, the adaptive normalization method uses the one-hot encoded semantic layout to generate the first latent space and the external attention uses the RGB encoded semantic layout to generate the second latent space. Two latent spaces control the shape of objects and the positional relationship between objects in the generated image. The graph attention (GAT) is added to the generator to strengthen the relationship between different categories in the generated image. A graph convolutional segmentation network (GSeg) is designed to learn information for each category. Experiments on several challenging datasets demonstrate the advantages of our method over existing approaches, regarding both visual quality and the representative evaluating criteria.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135597062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-performance computing for static security assessment of large power systems 大型电力系统静态安全评估的高性能计算

4区计算机科学

Connection Science Pub Date : 2023-10-04 DOI: 10.1080/09540091.2023.2264537

Venkateswara Rao Kagita, Sanjaya Kumar Panda, Ram Krishan, P. Deepak Reddy, Jabba Aswanth

{"title":"High-performance computing for static security assessment of large power systems","authors":"Venkateswara Rao Kagita, Sanjaya Kumar Panda, Ram Krishan, P. Deepak Reddy, Jabba Aswanth","doi":"10.1080/09540091.2023.2264537","DOIUrl":"https://doi.org/10.1080/09540091.2023.2264537","url":null,"abstract":"Contingency analysis (CA) is one of the essential tools for the optimal design and security assessment of a reliable power system. However, its computational requirements rise with the growth of distributed generations in the interconnected power system. As CA is a complex and computationally intensive problem, it requires a fast and accurate calculation to ensure the secure operation. Therefore, efficient mathematical modelling and parallel programming are key to efficient static security analysis. This paper proposes a parallel algorithm for static CA that uses both central processing units (CPUs) and graphical processing units (GPUs). To enhance the accuracy, AC load flow is used, and parallel computation of load flow is done simultaneously, with efficient screening and ranking of the critical contingencies. We perform extensive experiments to evaluate the efficacy of the proposed algorithm. As a result, we establish that the proposed parallel algorithm with high-performance computing (HPC) computing is much faster than the traditional algorithms. Furthermore, the HPC experiments were conducted using the national supercomputing facility, which demonstrates the proposed algorithm in the context of N−1 and N−2 static CA with immense power systems, such as the Indian northern regional power grid (NRPG) 246-bus and the polish 2383-bus networks.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"303 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neighbor interaction-based personalised transfer for cross-domain recommendation 基于邻居交互的跨域推荐个性化转移

4区计算机科学

Connection Science Pub Date : 2023-09-29 DOI: 10.1080/09540091.2023.2263664

Kelei Sun, Yingying Wang, Mengqi He, Huaping Zhou, Shunxiang Zhang

{"title":"Neighbor interaction-based personalised transfer for cross-domain recommendation","authors":"Kelei Sun, Yingying Wang, Mengqi He, Huaping Zhou, Shunxiang Zhang","doi":"10.1080/09540091.2023.2263664","DOIUrl":"https://doi.org/10.1080/09540091.2023.2263664","url":null,"abstract":"Mapping-based cross-domain recommendation (CDR) can effectively tackle the cold-start problem in traditional recommender systems. However, existing mapping-based CDR methods ignore data-sparse users in the source domain, which may impact the transfer efficiency of their preferences. To this end, this paper proposes a novel method named Neighbor Interaction-based Personalized Transfer for Cross-Domain Recommendation (NIPT-CDR). This proposed method mainly contains two modules: (i) an intra-domain item supplementing module and (ii) a personalised feature transfer module. The first module introduces neighbour interactions to supplement the potential missing preferences for each source domain user, particularly for those with limited observed interactions. This approach comprehensively captures the preferences of all users. The second module develops an attention mechanism to guide the knowledge transfer process selectively. Moreover, a meta-network based on users' transferable features is trained to construct personalised mapping functions for each user. The experimental results on two real-world datasets show that the proposed NIPT-CDR method achieves significant performance improvements compared to seven baseline models. The proposed model can provide more accurate and personalised recommendation services for cold-start users.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficiency control strategy of dual-motor multi-gear drive algorithm 双电机多齿轮驱动算法的高效控制策略

4区计算机科学

Connection Science Pub Date : 2023-09-27 DOI: 10.1080/09540091.2023.2249264

Lijun Xiao, Wei Liang, Jiahong Cai, Ming Wang, Jiahong Xiao, Yinyan Gong, Weigang Zhang

引用次数: 0

CPW-DICE: a novel center and pixel-based weighting for damage segmentation CPW-DICE:一种新的基于中心和像素的损伤分割加权方法

4区计算机科学

Connection Science Pub Date : 2023-09-26 DOI: 10.1080/09540091.2023.2259115

Yunus Abdi, Ömer Küllü, Mehmet Kıvılcım Keleş, Berk Gökberk

{"title":"CPW-DICE: a novel center and pixel-based weighting for damage segmentation","authors":"Yunus Abdi, Ömer Küllü, Mehmet Kıvılcım Keleş, Berk Gökberk","doi":"10.1080/09540091.2023.2259115","DOIUrl":"https://doi.org/10.1080/09540091.2023.2259115","url":null,"abstract":"Reliable evaluation of damage in vehicles is a primary concern in the insurance industry. Consequently, solutions enhanced with Artificial Intelligence (AI) have become the norm. During the assessment, precise damage segmentation plays a crucial role. Dent is a type of damage that can commonly occur in vehicles. It is difficult to pinpoint and tends to blend in with the background. This paper proposes a novel loss function to improve dent segmentation accuracy in vehicle insurance claims. Centre and Pixel-based Weighted DICE (CPW-DICE) is a loss function that performs pixel-based weighting. The CPW-DICE aims to concentrate on the centre of the dent damage to lessen faulty segmentations. CPW-DICE generates a weight mask during training by employing ground truth (GT) and prediction masks. Simultaneously, the weight mask is incorporated into DICE loss. Experiments conducted on our comprehensive internal dataset show a 3% improvement in Intersection over Union (IoU) score for three state-of-the-art (SOTA) approaches compared to DICE loss. Finally, CPW-DICE is evaluated in similar tasks to demonstrate its benefits beyond car damage segmentation.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134885411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting susceptible communities and individuals in hospital contact networks: a model based on social network analysis 检测医院接触网络中的易感社区和个人:基于社会网络分析的模型

IF 5.3 4区计算机科学

Connection Science Pub Date : 2023-08-22 DOI: 10.1080/09540091.2023.2236810

Yixuan Yang, Sony Peng, Sophort Siet, Sadriddinov Ilkhomjon, Vilakone Phonexay, Seok-Hoon Kim, Doosoon Park

引用次数: 0

Design of advanced intrusion detection systems based on hybrid machine learning techniques in hierarchically wireless sensor networks 分层无线传感器网络中基于混合机器学习技术的高级入侵检测系统设计

IF 5.3 4区计算机科学

Connection Science Pub Date : 2023-08-22 DOI: 10.1080/09540091.2023.2246703

Gebrekiros Gebreyesus Gebremariam, J. Panda, S. Indu

引用次数: 0

Real-time reading system for pointer meter based on YolactEdge 基于YolactEdge的指针式仪表实时读数系统

IF 5.3 4区计算机科学

Connection Science Pub Date : 2023-08-22 DOI: 10.1080/09540091.2023.2241669

Chengjun Yang, Ruijie Zhu, Xinde Yu, Ce Yang, Lijun Xiao, Scott Fowler

引用次数: 0