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Towards understanding residual and dilated dense neural networks via convolutional sparse coding 认领
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作者 Zhiyang Zhang Shihua Zhang 《国家科学评论:英文版》 SCIE EI CAS CSCD 2021年第3期122-134,共13页
Convolutional neural network(CNN)and its variants have led to many state-of-the-art results in various fields.However,a clear theoretical understanding of such networks is still lacking.Recently,a multilayer convoluti... Convolutional neural network(CNN)and its variants have led to many state-of-the-art results in various fields.However,a clear theoretical understanding of such networks is still lacking.Recently,a multilayer convolutional sparse coding(ML-CSC)model has been proposed and proved to equal such simply stacked networks(plain networks).Here,we consider the initialization,the dictionary design and the number of iterations to be factors in each layer that greatly affect the performance of the ML-CSC model.Inspired by these considerations,we propose two novel multilayer models:the residual convolutional sparse coding(Res-CSC)model and the mixed-scale dense convolutional sparse coding(MSD-CSC)model.They are closely related to the residual neural network(ResNet)and the mixed-scale(dilated)dense neural network(MSDNet),respectively.Mathematically,we derive the skip connection in the ResNet as a special case of a new forward propagation rule for the ML-CSC model.We also find a theoretical interpretation of dilated convolution and dense connection in the MSDNet by analyzing the MSD-CSC model,which gives a clear mathematical understanding of each.We implement the iterative soft thresholding algorithm and its fast version to solve the Res-CSC and MSD-CSC models.The unfolding operation can be employed for further improvement.Finally,extensive numerical experiments and comparison with competing methods demonstrate their effectiveness. 展开更多
关键词 convolutional neural network convolutional sparse coding residual neural network mixed-scale dense neural network dilated convolution dense connection
Identifying influential nodes in social networks via community structure and influence distribution difference 认领
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作者 Zufan Zhang Xieliang Li Chenquan Gan 《数字通信与网络:英文版》 SCIE CSCD 2021年第1期131-139,共9页
This paper aims to effectively solve the problem of the influence maximization in social networks.For this purpose,an influence maximization method that can identify influential nodes via the community structure and t... This paper aims to effectively solve the problem of the influence maximization in social networks.For this purpose,an influence maximization method that can identify influential nodes via the community structure and the influence distribution difference is proposed.Firstly,the network embedding-based community detection approach is developed,by which the social network is divided into several high-quality communities.Secondly,the solution of influence maximization is composed of the candidate stage and the greedy stage.The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm,and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm.Finally,experimental results demonstrate the superiority of the proposed method compared with existing methods,from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time. 展开更多
关键词 Social network Community detection Influence maximization Network embedding Influence distribution difference
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A Novel SAR Image Ship Small Targets Detection Method 认领
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作者 Yu Song Min Li +3 位作者 Xiaohua Qiu Weidong Du Yujie He Xiaoxiang Qi 《电脑和通信(英文)》 2021年第2期57-71,共15页
To satisfy practical requirements of high real-time accuracy and low computational complexity of synthetic aperture radar (SAR) image ship small target detection, this paper proposes a small ship target detection meth... To satisfy practical requirements of high real-time accuracy and low computational complexity of synthetic aperture radar (SAR) image ship small target detection, this paper proposes a small ship target detection method based on the improved You Only Look Once Version 3 (YOLOv3). The main contributions of this study are threefold. First, the feature extraction network of the original YOLOV3 algorithm is replaced with the VGG16 network convolution layer. Second, general convolution is transformed into depthwise separable convolution, thereby reducing the computational cost of the algorithm. Third, a residual network structure is introduced into the feature extraction network to reuse the shallow target feature information, which enhances the detailed features of the target and ensures the improvement in accuracy of small target detection performance. To evaluate the performance of the proposed method, many experiments are conducted on public SAR image datasets. For ship targets with complex backgrounds and small ship targets in the SAR image, the effectiveness of the proposed algorithm is verified. Results show that the accuracy and recall rate improved by 5.31% and 2.77%, respectively, compared with the original YOLOV3. Furthermore, the proposed model not only significantly reduces the computational effort, but also improves the detection accuracy of ship small target. 展开更多
关键词 The SAR Images The Neural Network Ship Small Target Target Detection
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A review:Photonics devices,architectures,and algorithms for optical neural computing 认领 被引量:1
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作者 Shuiying Xiang Yanan Han +15 位作者 Ziwei Song Xingxing Guo Yahui Zhang Zhenxing Ren Suhong Wang Yuanting Ma Weiwen Zou Bowen Ma Shaofu Xu Jianji Dong Hailong Zhou Quansheng Ren Tao Deng Yan Liu Genquan Han Yue Hao 《半导体学报:英文版》 EI CAS CSCD 2021年第2期64-79,共16页
The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era.Photonics neuromorphic computing has attracted lots of attention due to t... The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era.Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed,wide bandwidth,and massive parallelism.Here,we offer a review on the optical neural computing in our research groups at the device and system levels.The photonics neuron and photonics synapse plasticity are presented.In addition,we introduce several optical neural computing architectures and algorithms including photonic spiking neural network,photonic convolutional neural network,photonic matrix computation,photonic reservoir computing,and photonic reinforcement learning.Finally,we summarize the major challenges faced by photonic neuromorphic computing,and propose promising solutions and perspectives. 展开更多
关键词 photonics neuron photonic STDP photonic spiking neural network optical reservoir computing optical convolutional neural network neuromorphic photonics
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Closer:Scalable Load Balancing Mechanism for Cloud Datacenters 认领
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作者 Zixi Cui Pengshuai Cui +4 位作者 Yuxiang Hu Julong Lan Fang Dong Yunjie Gu Saifeng Hou 《中国通信:英文版》 SCIE CSCD 2021年第4期198-212,共15页
Cloud providers(e.g.,Google,Alibaba,Amazon)own large-scale datacenter networks that comprise thousands of switches and links.A loadbalancing mechanism is supposed to effectively utilize the bisection bandwidth.Both Eq... Cloud providers(e.g.,Google,Alibaba,Amazon)own large-scale datacenter networks that comprise thousands of switches and links.A loadbalancing mechanism is supposed to effectively utilize the bisection bandwidth.Both Equal-Cost Multi-Path(ECMP),the canonical solution in practice,and alternatives come with performance limitations or significant deployment challenges.In this work,we propose Closer,a scalable load balancing mechanism for cloud datacenters.Closer complies with the evaluation of technology including the deployment of Clos-based topologies,overlays for network virtualization,and virtual machine(VM)clusters.We decouple the system into centralized route calculation and distributed route decision to guarantee its flexibility and stability in large-scale networks.Leveraging In-band Network Telemetry(INT)to obtain precise link state information,a simple but efficient algorithm implements a weighted ECMP at the edge of fabric,which enables Closer to proactively map the flows to the appropriate path and avoid the excessive congestion of a single link.Closer achieves 2 to 7 times better flow completion time(FCT)at 70%network load than existing schemes that work with same hardware environment. 展开更多
关键词 cloud datacenters load balancing programmable network INT overlay network
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Two-Stage Point Cloud Super Resolution with Local Interpolation and Readjustment via Outer-Product Neural Network 认领
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作者 WANG Guangyu XU Gang +1 位作者 WU Qing WU Xundong 《系统科学与复杂性学报:英文版》 SCIE EI CSCD 2021年第1期68-82,共15页
This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method ... This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method to increase the density and uniformity of the target point cloud. For the second stage, the authors employ an outer-product neural network to readjust the position of points that are inserted at the first stage. Comparison examples are given to demonstrate that the proposed framework achieves a better accuracy than existing state-of-art approaches, such as PU-Net, Point Net and DGCNN(Source code is available at https://github.com/qwerty1319/PC-SR). 展开更多
关键词 Neural network outer-product network point cloud super resolution
Computing Power Network:The Architecture of Convergence of Computing and Networking towards 6G Requirement 认领
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作者 Xiongyan Tang Chang Cao +4 位作者 Youxiang Wang Shuai Zhang Ying Liu Mingxuan Li Tao He 《中国通信:英文版》 SCIE CSCD 2021年第2期175-185,共11页
In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computi... In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on. 展开更多
关键词 6G edge computing cloud computing convergence of cloud and network computing power network
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Flex Ethernet Technology and Application in 5G Mobile Transport Network 认领
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作者 Meng Zhang 《中国通信:英文版》 SCIE CSCD 2021年第2期250-258,共9页
With the advent of 5G era,the rise of cloud services,virtual reality/virtual reality(AR/VR),vehicle networking and other technologies has put forward new requirements for the bandwidth and delay of the bearer network.... With the advent of 5G era,the rise of cloud services,virtual reality/virtual reality(AR/VR),vehicle networking and other technologies has put forward new requirements for the bandwidth and delay of the bearer network.Traditional Ethernet technology cannot meet the new requirements very well.Flex Ethernet(FlexE)technology has emerged as the times require.This paper introduces the background,standardization process,functional principle,application mode and technical advantages of FlexE technology,and finally analyses its application prospects and shortcomings in 5G mobile transport network. 展开更多
关键词 Flex Ethernet(FlexE) 5G mobile transport network network slicing
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Interpreting Nestedness and Modularity Structures in Affiliation Networks: An Application in Knowledge Networks Formed by Software Project Teams 认领
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作者 Jorge Luiz dos Santos Renelson Ribeiro Sampaio 《社交网络(英文)》 2021年第1期1-18,共18页
An understanding of the knowledge creation and diffusion process in the organizational context is extremely relevant. Because from this understanding, organizations can restructure processes, reorient teams and implem... An understanding of the knowledge creation and diffusion process in the organizational context is extremely relevant. Because from this understanding, organizations can restructure processes, reorient teams and implement methodologies to assist in the construction of an evolutionary process of knowledge creation and diffusion aimed at sustainable growth and innovation. The theory of complex social networks has been applied in several fields to help understand organizational cognitive processes. However, these approaches still insipiently consider the analysis of the nestedness and modularity of the studied networks. In this article, we presented an approach that sought to identify patterns of nestedness and modularity in networks of affiliation of people in projects in the organizational context. The study sought to identify these patterns in affiliation networks in a public organization providing information technology services in the period from 2006 to 2013. The detection of these patterns was performed using the NODF (Nestedness metric based on Overlap and Decreasing Fill) algorithm described by <a href="#ref1">[1]</a>. The nestedness and modularity metrics can influence patterns of knowledge creation and diffusion in formal and informal networks constituted for the execution of projects in organizations. This study showed that the network structures of the organization during the study period presented a high degree of nestedness, and it was possible to identify combined structures of nestedness and modularity. 展开更多
关键词 Social Network Analysis Affiliation Networks MODULARITY NESTEDNESS
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A Topological Evolution Model Based on the Attraction of the Motif Vertex 认领
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作者 Xing Li Shuxin Liu +1 位作者 Yuhang Zhu Yingle Li 《中国通信:英文版》 SCIE CSCD 2021年第4期27-39,共13页
As a fundamental problem in the field of the network science,the study of topological evolution model is of great significance for revealing the inherent dynamics and mechanisms of complex network evolution.In order t... As a fundamental problem in the field of the network science,the study of topological evolution model is of great significance for revealing the inherent dynamics and mechanisms of complex network evolution.In order to study the influence of different scales of preferential attachment on topological evolution,a topological evolution model based on the attraction of the motif vertex is proposed.From the perspective of network motif,this model proposes the concept of attraction of the motif vertex based on the degree of the motif,quantifies the influence of local structure on the node preferential attachment,and performs the preferential selection of the new link based on the Local World model.The simulation experiments show that the model has the small world characteristic apparently,and the clustering coefficient varies with the scale of the local world.The degree distribution of the model changes from power-law distribution to exponential distribution with the change of parameters.In some cases,the piecewise power-law distribution is presented.In addition,the proposed model can present a network with different matching patterns as the parameters change. 展开更多
关键词 complex network topological evolution model network motif
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Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks 认领
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作者 Husam A.H.Al-Najjar Biswajeet Pradhan 《地学前缘:英文版》 SCIE CAS CSCD 2021年第2期625-637,共13页
In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory... In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive 展开更多
关键词 Landslide susceptibility INVENTORY Machine learning Generative adversarial network Convolutional neural network Geographic information system
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Adversarial network embedding using structural similarity 认领
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作者 Zihan ZHOU Yu GU Ge YU 《中国计算机科学前沿:英文版》 SCIE EI CSCD 2021年第1期223-232,共10页
Network embedding which aims to embed a given network into a low-dimensional vector space has been proved effective in various network analysis and mining tasks such as node classification,link prediction and network ... Network embedding which aims to embed a given network into a low-dimensional vector space has been proved effective in various network analysis and mining tasks such as node classification,link prediction and network visualization.The emerging network embedding methods have shifted of emphasis in utilizing mature deep learning models.The neural-network based network embedding has become a mainstream solution because of its high eficiency and capability of preserv-ing the nonlinear characteristics of the network.In this paper,we propose Adversarial Network Embedding using Structural Similarity(ANESS),a novel,versatile,low-complexity GAN-based network embedding model which utilizes the inherent vertex-to-vertex structural similarity attribute of the network.ANESS learns robustness and ffective vertex embeddings via a adversarial training procedure.Specifically,our method aims to exploit the strengths of generative adversarial networks in generating high-quality samples and utilize the structural similarity identity of vertexes to learn the latent representations of a network.Meanwhile,ANESS can dynamically update the strategy of generating samples during each training iteration.The extensive experiments have been conducted on the several benchmark network datasets,and empirical results demon-strate that ANESS significantly outperforms other state-of-theart network embedding methods. 展开更多
关键词 network embedding structural similarity generative adversarial network
Using multilayer network analysis to explore the temporal dynamics of collective behavior 认领
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作者 David N.FISHER Noa PINTER-WOLLMAN 《动物学报:英文版》 SCIE CAS CSCD 2021年第1期71-80,共10页
Social organisms often show collective behaviors such as group foraging or movement.Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals.When social... Social organisms often show collective behaviors such as group foraging or movement.Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals.When social interactions change over time,collective behaviors may change because these behaviors emerge from interactions among individuals.Despite the importance of,and growing interest in,the temporal dynamics of social interactions,it is not clear how to quantify changes in interactions over time or measure their stability.Furthermore,the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent.Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors.We found that social interactions changed over time at a constant rate.Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed.Individuals that maintained a large and stable number of connections,despite changes in network structure,were the boldest individuals in the group.Therefore,social interactions and boldness are linked across time,but group collective behavior is not influenced by the stability of the social network.Our work demonstrates that dynamic social networks can be modeled in a multilayer framework.This approach may reveal biologically important temporal changes to social structure in other systems. 展开更多
关键词 collective behavior dynamic network multilayer network multiplex social stability Stegodyphus
政府网络公共关系优化研究 认领
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作者 黄冠东 《中小企业管理与科技》 2021年第4期112-113,共2页
以目前政府新媒体形式中较为成熟的政务微博为切入点,从意识、流程、技术三个层面发现政府网络公关活动的问题,并基于此提出培养理论意识、加强制度化管理、技术成为强力助推器以及善用媒体提高公民理性的应对措施,以期优化政府网络公... 以目前政府新媒体形式中较为成熟的政务微博为切入点,从意识、流程、技术三个层面发现政府网络公关活动的问题,并基于此提出培养理论意识、加强制度化管理、技术成为强力助推器以及善用媒体提高公民理性的应对措施,以期优化政府网络公共关系。 展开更多
关键词 后真相 网络 政府公关 网络公关
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基于BERT的学术合作者推荐研究 认领
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作者 周亦敏 黄俊 《计算机技术与发展》 2021年第3期45-51,共7页
学术合作者推荐是学术大数据的一个有效应用。但是现存的方法忽略了学术研究者和研究主题间的上下文关系,因此不能推荐合适的合作者。该文提出了基于BERT的合作者推荐(BACR),旨在推荐高潜力的合作者以达到研究者的要求。为此,设计了一... 学术合作者推荐是学术大数据的一个有效应用。但是现存的方法忽略了学术研究者和研究主题间的上下文关系,因此不能推荐合适的合作者。该文提出了基于BERT的合作者推荐(BACR),旨在推荐高潜力的合作者以达到研究者的要求。为此,设计了一个新的推荐框架,它有两个基本组成部分:BERT(bidirectional encoder representations from transformers)预训练语言模型和逻辑回归模型(LR)。其中,BERT将研究者和研究主题联合表示得到句子层面的具有上下文关系的特征向量表示。LR将BERT输出的特征向量作为输入得到该样本为正类的概率,最后输出概率最大的前K个合作者信息。通过与基于Network Embedding的SDNE和TSE算法的对比实验,结果表明充分考虑了研究者和研究主题间的上下文关系的BERT模型得到了更好的特征向量表示,提高了合作者推荐的准确率。 展开更多
关键词 BERT模型 合作者推荐 逻辑回归模型 学术数据挖掘 Network Embedding
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An Oracle Bone Inscription Detector Based on Multi-Scale Gaussian Kernels 认领
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作者 Guoying Liu Shuanghao Chen +1 位作者 Jing Xiong Qingju Jiao 《应用数学(英文)》 2021年第3期224-239,共16页
The detection of Oracle Bone Inscriptions (OBIs) is one of the most fundamental tasks in the study of Oracle Bone, which aims to locate the positions of OBIs on rubbing images. The existing methods are based on the sc... The detection of Oracle Bone Inscriptions (OBIs) is one of the most fundamental tasks in the study of Oracle Bone, which aims to locate the positions of OBIs on rubbing images. The existing methods are based on the scheme of anchor boxes, involving complex network design and a great number of anchor boxes. In order to overcome the problem, this paper proposes a simpler but more effective OBIs detector by using an anchor-free scheme, where shape-adaptive Gaussian kernels are employed to represent the spatial regions of different OBIs. More specifically, to address the problem of misdetection caused by regional overlapping between some tightly distributed OBIs, the character regions are simultaneously represented by multiscale Gaussian kernels to obtain regions with sharp edges. Besides, based on the kernel predictions of different scales, a novel post-processing pipeline is used to obtain accurate predictions of bounding boxes. Experiments show that our OBIs detector has achieved significant results on the OBIs dataset, which greatly outperforms several mainstream object detectors in both speed and efficiency. Dataset is available at http://jgw.aynu.edu.cn. 展开更多
关键词 Oracle Bone Inscriptions Deep Learning Object Detection Hourglass Network
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Asian Food Image Classification Based on Deep Learning 认领
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作者 Bing Xu Xiaopei He Zhijian Qu 《电脑和通信(英文)》 2021年第3期10-28,共19页
To improve Asian food image classification accuracy, a method that combined Convolutional Block Attention Module (CBAM) with the Mobile NetV2, VGG16, and ResNet50 was proposed for Asian food image classification. Addi... To improve Asian food image classification accuracy, a method that combined Convolutional Block Attention Module (CBAM) with the Mobile NetV2, VGG16, and ResNet50 was proposed for Asian food image classification. Additionally, we proposed to use a mixed data enhancement algorithm (Mixup) to have a smoother discrimination ability. The effects of introducing the attention mechanism (CBAM) and using the mixed data enhancement algorithm (Mixup) were shown respectively through experimental comparison. The combination of these two and the final test set Top-1 accuracy rate reached 87.33%. Moreover, the information emphasized by CBAM was reflected through the visualization of the heat map. The results confirmed the classification method’s effectiveness and provided new ideas that improved Asian food image classification accuracy. 展开更多
关键词 Asian Food Image Classification Convolutional Neural Network Attention Mechanism Data Enhancement
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Development of an Ontology-Based Knowledge Network by Interconnecting Soil/Water Concepts/Properties, Derived from Standards Methods and Published Scientific References Outlining Infiltration/Percolation Process of Contaminated Water 认领
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作者 Stephanos D. V. Giakoumatos Anastasios K. T. Gkionakis 《地球科学和环境保护期刊(英文)》 2021年第1期25-52,共28页
The present work deals with the development of an Ontology-Based Knowledge Network of soil/water physicochemical & biological properties (soil/water concepts), derived from ASTM Standard Methods (ASTMi,n) and rele... The present work deals with the development of an Ontology-Based Knowledge Network of soil/water physicochemical & biological properties (soil/water concepts), derived from ASTM Standard Methods (ASTMi,n) and relevant scientific/applicable references (published papers—PPi,n) to fill up/bridge the gap of the information science between cited Standards and infiltration discipline conceptual vocabulary providing accordingly a dedicated/internal Knowledge Base (KB). This attempt constitutes an innovative approach, since it is based on externalizing domain knowledge in the form of Ontology-Based Knowledge Networks, incorporating standardized methodology in soil engineering. The ontology soil/water concepts (semantics) of the developed network correspond to soil/water physicochemical & biological properties, classified in seven different generations that are distinguished/located in infiltration/percolation process of contaminated water through soil porous media. The interconnections with arcs between corresponding concepts/properties among the consecutive generations are defined by the relationship of dependent and independent variables. All these interconnections are documented according to the below three ways: 1) dependent and independent variables interconnected by using the logical operator “<em>depends on</em>” quoting existent explicit functions and equations;2) dependent and independent variables interconnected by using the logical operator “<em>depends on</em>” quoting produced implicit functions, according to Rayleigh’s method of indices;3) dependent and independent variables interconnected by using the logical operator “<em>related to</em>” based on a logical dependence among the examined nodes-concepts-variables. The aforementioned approach provides significant advantages to semantic web developers and web users by means of prompt knowledge navigation, tracking, retrieval and usage. 展开更多
关键词 INFILTRATION PERCOLATION ASTM Standards Soil/Water Contamination Knowledge Base Ontology Network Semantics Porous Media
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Bangla Handwritten Character Recognition Using Extended Convolutional Neural Network 认领
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作者 Tandra Rani Das Sharad Hasan +2 位作者 Md. Rafsan Jani Fahima Tabassum Md. Imdadul Islam 《电脑和通信(英文)》 2021年第3期158-171,共14页
The necessity of recognizing handwritten characters is increasing day by day because of its various applications. The objective of this paper is to provide a sophisticated, effective and efficient way to recognize and... The necessity of recognizing handwritten characters is increasing day by day because of its various applications. The objective of this paper is to provide a sophisticated, effective and efficient way to recognize and classify Bangla handwritten characters. Here an extended convolutional neural network (CNN) model has been proposed to recognize Bangla handwritten characters. Our CNN model is tested on <span style="font-family:Verdana;">“</span><span style="font-family:Verdana;">BanglalLekha-Isolated</span><span style="font-family:Verdana;">”</span><span style="font-family:Verdana;"> dataset where there are 10 classes for digits, 11 classes for vowels and 39 classes for consonants. Our model shows accuracy of recognition as: 99.50% for Bangla digits, 93.18% for vowels, 90.00% for consonants and 92.25% for combined classes.</span> 展开更多
关键词 Loss and Accuracy Deep Neural Network Image Classification Noise Removal CNN and HCR
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Network analysis and spatial agglomeration of China’s high-speed rail: A dual network approach 认领
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作者 王微 杜文博 +2 位作者 李威翰 佟路 王姣娥 《中国物理B:英文版》 SCIE EI CAS CSCD 2021年第1期612-622,共11页
China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various rel... China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various relationships(i.e.,linkages) between them. In this paper, we first introduce a general dual network model, including a physical network(PN)and a logical network(LN) to provide a comparative analysis for China’s high-speed rail network via complex network theory. The PN represents a layout of stations and rail tracks, and forms the basis for operating all trains. The LN is a network composed of the origin and destination stations of each high-speed train and the train flows between them. China’s high-speed railway(CHSR) has different topological structures and link strengths for PN in comparison with the LN. In the study, the community detection is used to analyze China’s high-speed rail networks and several communities are found to be similar to the layout of planned urban agglomerations in China. Furthermore, the hierarchies of urban agglomerations are different from each other according to the strength of inter-regional interaction and intra-regional interaction, which are respectively related to location and spatial development strategies. Moreover, a case study of the Yangtze River Delta shows that the hub stations have different resource divisions and are major contributors to the gap between train departure and arrival flows. 展开更多
关键词 China’s high-speed rail dual network network analysis urban agglomeration
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