Intelligent reflecting surface (IRS) is an emerging technology for wireless communications, thanks to its powerful capability to engineer the radio environment. However, in practice, this benefit is attainable only wh...
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Retrieving unlabeled videos by textual queries, known as Ad-hoc Video Search (AVS), is a core theme in multimedia data management and retrieval. The success of AVS counts on cross-modal representation learning that en...
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On account of a large scale of dataset need to be annotated to fit for specific tasks, Zero-Shot Learning(ZSL) has invoked so much attention and got significant progress in recent research due to the prevalence of dee...
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ISBN:
(数字)9781728169262
ISBN:
(纸本)9781728169279
On account of a large scale of dataset need to be annotated to fit for specific tasks, Zero-Shot Learning(ZSL) has invoked so much attention and got significant progress in recent research due to the prevalence of deep neural networks. At present, ZSL is mainly solved through the utilization of auxiliary information, such as semantic attributes and text descriptions. And then, we can employ the mapping method to bridge the gap between visual and semantic space. However, due to the lack of effective use of auxiliary information, this problem has not been solved well. Inspired by previous work, we consider that visual space can be used as the embedding space to get a stronger ability to express the precise characteristics of semantic information. Meanwhile, we take into account that there are some noise attributes in the annotated information of public datasets that need to be processed. Based on these considerations, we propose an end-to-end method with convolutional architecture, instead of conventionally linear projection, to provide a deep representation for semantic information to solve ZSL. Semantic features would express more detailed and precise information after being feed into our method. Besides, we use word embedding to generate some superclasses for original classes and propose a new loss function for these superclasses to assist in training. Experiments show that our method can get decent improvements for ZSL and Generalized Zero-Shot Learning(GZSL) on several public datasets.
With the high mortality rate for the cases of malignant tumors, the discovery and early treatment of cancer is critical to improving the 5-year survival rate of cancer. The biggest challenge in control and prevention ...
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In recent years, data center network is used for transmission, storage and processing of big data, which plays an important role for applications in cloud computing and CDN distribution. Network topology and routing a...
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Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise *...
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Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.
Convolutional Neural Networks (CNNs)-guided deep models have obtained impressive performance for image representation, however the representation ability may still be restricted and usually needs more epochs to make t...
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ISBN:
(纸本)9781665423991
Convolutional Neural Networks (CNNs)-guided deep models have obtained impressive performance for image representation, however the representation ability may still be restricted and usually needs more epochs to make the model converge in training, due to the useful information loss during the convolution and pooling operations. We therefore propose a general feature recovery layer, termed Low-rank Deep Feature Recovery (LDFR), to enhance the representation ability of the convolutional features by seamlessly integrating low-rank recovery into CNNs, which can be easily extended to all existing CNNs-based models. To be specific, to recover the lost information during the convolution operation, LDFR aims at learning the low-rank projections to embed the feature maps onto a low-rank subspace based on some selected informative convolutional feature maps. Such low-rank recovery operation can ensure all convolutional feature maps to be reconstructed easily to recover the underlying subspace with more useful and detailed information discovered, e.g., the strokes of characters or the texture information of clothes can be enhanced after LDFR. In addition, to make the learnt low-rank subspaces more powerful for feature recovery, we design a fusion strategy to obtain a generalized subspace, which averages over all learnt sub-spaces in each LDFR layer, so that the convolutional feature maps in test phase can be recovered effectively via low-rank embedding. Extensive results on several image datasets show that existing CNNs-based models equipped with our LDFR layer can obtain better performance.
Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accid...
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Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. Security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.
Based on 3D printing technology, multi-core doped silica optical fibre has been fabricated. The demonstration heralds a new fibre manufacturing milestone one that enables the design of fibres not previously feasible. ...
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Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical roots, ...
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ISBN:
(数字)9781728181561
ISBN:
(纸本)9781728181578
Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical roots, and explore the origins of a family more easily. However, the multi-type, multisource dynamic changes and specialized nature of genealogical data bring challenges to the development of contemporary knowledge graph models. Applying existing methods to genealogical data can result in problems of overlooking certain specialized vocabulary and dynamic properties such as personal experiences. In this paper, we propose a genealogical knowledge graph model GKGM that combines HAO intelligence (h uman intelligence + a rtificial intelligence + o rganizational intelligence) and ontology granularity division technology to address the above problems. Furthermore, a method of applying the model to construct genealogical knowledge graphs is demonstrated, and an experiment conducted on a real-world genealogical dataset verifies the feasibility and effectiveness of the model.
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