The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure prof...
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Build system,which can convert source codes into applications,is essential for the development of *** general build systems that relying on single physical or cloud host to run bring problems such as system security,r...
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Build system,which can convert source codes into applications,is essential for the development of *** general build systems that relying on single physical or cloud host to run bring problems such as system security,resource shortage,overload,and low availability in the face of massive build *** modularizing and streamlining the steps during a build process,this paper proposes a system that introduces container technology and then builds a large-scale,real-time,and huge-concurrency supported build system based on Kubernetes[1].The system provides a highly scalable and feature-stable cloud architecture that supports huge concurrency with lower resource ***,the system controls programs' behaviors very well to avoid potential security and resource issues and shows excellent performance in concurrency,scalability,security,and load balance even when handling a large number of build tasks.
MOOCs have attracted a large number of learners with different education background all over the world. Despite its increasing popularity, MOOCs still suffer from the problem of high drop-out rate. One important reaso...
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Mobile devices play an important role in our everyday lives, but they also bring great security threats. Deep packet inspection (DPI) is one of the most efficient methods to detect the malicious information hidden in ...
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The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopte...
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JointCloud computing is a new generation cloud computing model based on collaboration among Cloud Service Providers, making resources from multiple clouds deeply integrated., and supporting customize cloud service. To...
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Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preve...
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Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preventing the tracker from failure in these two situations by integrating the depth information into a correlation filter based tracker. By using RGB-D data, we construct a depth context model to reveal the spatial correlation between the target and its surrounding regions. Furthermore, we adopt a region growing method to make our tracker robust to occlusion and scale variation. Additional optimizations such as a model updating scheme are applied to improve the performance for longer video sequences. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favourably against state-of-the-art algorithms.
Convolution neural network models are widely used in image classification tasks. However, the running time of such models is so long that it is not the conforming to the strict real-time requirement of mobile devices....
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Convolution neural network models are widely used in image classification tasks. However, the running time of such models is so long that it is not the conforming to the strict real-time requirement of mobile devices. In order to optimize models and meet the requirement mentioned above, we propose a method that replaces the fully-connected layers of convolution neural network models with a tree classifier. Specifically, we construct a Visual Confusion Label Tree based on the output of the convolution neural network models, and use a multi-kernel SVM plus classifier with hierarchical constraints to train the tree classifier. Focusing on those confusion subsets instead of the entire set of categories makes the tree classifier more discriminative and the replacement of the fully-connected layers reduces the original running time. Experiments show that our tree classifier obtains a significant improvement over the state-of-the-art tree classifier by 4.3% and 2.4% in terms of top-1 accuracy on CIFAR-100 and ImageNet datasets respectively. Additionally, our method achieves 124× and 115× speedup ratio compared with fully-connected layers on AlexNet and VGG16 without accuracy decline.
In the past decade, multi-robot simultaneous localization and mapping (SLAM) has been widely studied. However, the problem of collaborative SLAM with a large number of robots, such as dozens of robots, is far from bei...
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In the past decade, multi-robot simultaneous localization and mapping (SLAM) has been widely studied. However, the problem of collaborative SLAM with a large number of robots, such as dozens of robots, is far from being well solved. The challenges stem from not only the computation complexity in large-scale map merging but also the inefficiency to enable the parallel computing in this process, which is indispensable for us to make avail of the frontier of computing technology such as powerful cloud infrastructure. To effectively address these challenges, especially the latter one, we propose a scalable and real-time multi-robot visual SLAM framework based on the cloud robotic paradigm. The prominent feature of our framework is that it can distribute the SLAM process to multiple computing hosts in a cluster, which enables map building in parallel. To eliminate the bottleneck from data sharing between different sub-tasks, we also introduce diversified messaging pattern for various messaging scenarios, as well as the consistency policies for map data. The evaluations on the prototype of our framework, have shown that our method can do support as many as 256 robot entities simultaneously, without any compromising on the precision of poses estimation and map building.
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consis...
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