Chinese Named Entity Recognition (NER) for Electronic Medical Records (EMR) is a fundamental task in building a digital hospital and is widely considered to be a sequence annotation problem in the Natural Language Pro...
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Underwater Image transformation uses deep learning to generate images with the target domain and the source domain. It can quickly and accurately solve transformation problems such as image blurring for underwater ima...
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As an important model of machine learning, Bayesian networks are widely applied into medical diagnosis and achieve good performances in practical applications. Compared to black-box models, Bayesian networks can clear...
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Specular highlight usually causes serious information degradation,which leads to the failure of many computer vision *** have proposed a novel bifurcated convolution neural network to tackle the problem of high reflec...
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Specular highlight usually causes serious information degradation,which leads to the failure of many computer vision *** have proposed a novel bifurcated convolution neural network to tackle the problem of high reflectivity image information degradation.A two-stage process is proposed for the extraction and elimination of the specular highlight features,with the procedure starting at a coarse level and progressing towards a finer level,to ensure the generated diffuse images are less affected by visual artifacts and information distortions.A bifurcated feature selection module is designed to remove the specular highlight features,thereby enhancing the detection capability of the *** experiments on two types of challenging datasets demonstrate that our method outperforms state-of-the-art approaches for specular highlight detection and *** effectiveness of the proposed bifurcated feature selection module and the overall network is also verified.
Medical images are complex, and the annotation of medical images requires high expertise. It would be time-consuming and costly to annotate directly for experts. As a result, one of the primary challenges currently fa...
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Ontology matching plays a crucial role in the realm of ontology engineering, specifically in ontology development and integration. It serves the purpose of resolving the issue of heterogeneous semantics between ontolo...
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Existing action recognition methods based on event cameras have not fully exploited the advantages of event cameras,such as compressing event streams into frames for subsequent calculation,which greatly sacrifices the...
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Existing action recognition methods based on event cameras have not fully exploited the advantages of event cameras,such as compressing event streams into frames for subsequent calculation,which greatly sacrifices the time information of event ***,the conventional PointCloud-based methods suffer from large computational complexity while processing event data,which make it difficult to handle long-term *** tackle the above problems,we propose a dynamic graph memory-boosting recurrent neural network(DG-MBRNN).The proposed DG-MBRNN splits the event stream into sequential graph data for preserving structural information,then uses the recurrent neural network(RNN)with boosting spatiotemporal memory to handle long-term sequences of *** addition,the proposed method introduces a dynamic reorganization mechanism for the graph based on the distances of features,which can effectively increase the ability to extract local *** order to cope with the situation that the existing datasets have too simple actions and too limited categories,we propose a new event-based dataset containing 36 complex *** dataset will greatly promote the development of event-based action recognition *** results show the effectiveness of the proposed method in completing the event-based action recognition task.
Medical reports play an important role in diagnosing a patient’s illness. However, writing medical reports is time-consuming and labor-intensive, and writing high-quality medical reports often requires extensive clin...
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The devices in the Internet of things(Io T) gain capability of sustainable operation when they harvest energy from ambient sources. Fluctuation in the harvested energy may cause the energy-harvesting IoT devices to su...
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The devices in the Internet of things(Io T) gain capability of sustainable operation when they harvest energy from ambient sources. Fluctuation in the harvested energy may cause the energy-harvesting IoT devices to suffer from frequent energy shortage, which may bring in intolerable packet delay or packet discarding. It is important to design a low-delay packet delivery scheme that adapts to variation in the harvested *** this paper, we present the timely data delivery(TDD)scheme for the IoT devices. Using Markov chain, we develop a probability model for the TDD scheme, which leads to the expected number of packets delivered in an operation cycle, the expected numbers of packets waiting in the data buffer in an operation cycle and an energy-harvesting cycle, and the expected packet delay. Additionally, we formulate the optimization problem that minimizes the packet delay in the TDD scheme, and the solution to the optimization problem yields the optimal parameters for the IoT devices to determine when to harvest energy and when to deliver data under the TDD *** simulation results show that the proposed TDD scheme outperforms the existing schemes in terms of packet delay.
The primary objective of this study is to investigate the impact of different types of knowledge coupling, complementary knowledge coupling, and alternative knowledge coupling, on the innovation performance of knowled...
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The primary objective of this study is to investigate the impact of different types of knowledge coupling, complementary knowledge coupling, and alternative knowledge coupling, on the innovation performance of knowledge-intensive enterprises. The research further explores the mediating role of dual innovation, encompassing both exploratory innovation and exploitative innovation, in the relationship between knowledge coupling and enterprises innovation performance during the process of knowledge creation. Additionally, the study analyzes the moderating effect of environmental uncertainty on the link between dual innovation and innovation performance. The research sample is derived from survey data collected from 315 knowledge-intensive enterprises in China. The analytical approach employed is a hierarchical regression analysis model to test the proposed model. Results indicate that both knowledge coupling and dual innovation exert positive effects on innovation performance. Notably, exploratory innovation and exploitative innovation assume distinct mediating roles in the relationship between knowledge coupling and innovation performance, thereby constituting crucial factors in the practical realm of enterprise innovation. Regarding environmental uncertainty, the study reveals a significant moderating effect on the relationship between dual innovation and enterprises innovation performance, with varying degrees of influence. Environmental uncertainty positively regulates exploratory innovation and innovation performance, whereas it exerts a negative regulatory effect on exploitative innovation and firm innovation performance. The empirical findings of this study hold substantial significance for decision-makers and regulators within knowledge-intensive enterprises, offering insights into the consideration of both exploratory and exploitative innovation when pursuing innovation performance. Furthermore, the study contributes a fresh perspective to the fields of knowled
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