With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high...
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The number of experimental data obtained under different conditions is large, and there are some differences in the distribution characteristics of samples. If a large number of different types of data are mixed and t...
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The number of experimental data obtained under different conditions is large, and there are some differences in the distribution characteristics of samples. If a large number of different types of data are mixed and the network communication Effectiveness evaluation is carried out, the characteristics of some samples may be drowned out, on the other hand, the information of many experimental samples is not fully utilized. In order to solve this problem, this paper proposes a method of network communication effectiveness evaluation based on multi-source information fusion. This method firstly analyzes the communication success rate of the cell communication node by longitudinal fusion of experimental information. Based on this, the confidence degree is used to construct the weighting coefficient, and the whole communication success rate of the network information system is evaluated by the experiment information of multiple nodes. Thus, the comprehensive evaluation of the communication efficiency of the unit node and the network Information system is realized.
This paper introduces a novel framework, the unified closest point method (CPM) using the least-squares generalized finite difference method (LS-GFDM), to solve the surface partial differential equations (PDEs). Our a...
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DRAM is a significant source of server power consumption especially when the server runs memory intensive applications. Current power aware scheduling assumes that DRAM is as energy proportional as other components. H...
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Currently, many cloud providers deploy their big data processing systems as cloud services, which helps users conveniently manage and process their data in clouds. Among different service providers’ big data processi...
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Visual Question Answering (VQA) requires a finegrained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective 'co-attention'...
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ISBN:
(纸本)9781728132945
Visual Question Answering (VQA) requires a finegrained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective 'co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the question-guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-of-the-art. Our best single model delivers 70.63% overall accuracy on the test-dev set.
Text entry is an imperative issue to be addressed in current entry systems for virtual environments (VEs). The entry method using a physical keyboard is still the most dominant choice for an efficient interaction rega...
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ISBN:
(纸本)9781538675939;9781538675922
Text entry is an imperative issue to be addressed in current entry systems for virtual environments (VEs). The entry method using a physical keyboard is still the most dominant choice for an efficient interaction regarding text entry. In this paper, we propose a typing system with a style of mixed reality, which is called HiKeyb, and it possesses a similar high-efficiency with the single physical keyboard in the real environment. The HiKeyb system consists of a depth camera, a pose tracking module, a head-mounted display (HMD), a QWERTY keyboard and a black table mat. This system can guarantee the entry efficiency and the amenity by not only introducing the force feedback from a movable physical keyboard, but also improving the immersion with the real hand image. In addition, the infrared absorption material helps improve the robustness of the system against different lighting environments. Experiments have proved that users wearing HMDs in Virtual Phrases session can achieve an entry rate of 23.1 words per minute and an error rate of 2.76%, and the rate ratio of virtual reality to real world is 78% when typing phrases. Besides, we find that the proposed system can provide a relatively close entry efficiency to that using a pure physical keyboard in the real environment.
Recently, reliability prediction problems have been sufficiently studied by applying various failures time series prediction models. However, these models' performance could easily reach the bottleneck on account ...
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Recently, reliability prediction problems have been sufficiently studied by applying various failures time series prediction models. However, these models' performance could easily reach the bottleneck on account of the noise interference of the failures data. This paper focuses on the trend component of failures time series data as we believe its noiseless curve will achieve better prediction results and help to provide scientific guidance for reliability activities. We propose a trend prediction method which mainly involves two steps, trend extraction by singular spectrum analysis(SSA) and noise test, and trend prediction by support vector machines regression(SVR). Moreover, we further develop a multi-layer grid search algorithm to obtain optimized parameters of SSA and SVR models. The performance of the proposed method is measured against other time series processing techniques such as seasonal-trend decomposition procedure based on loess(STL), Holt-Winters, autoregressive moving average(ARMA), multiple linear regression(MLR) and k-nearest neighbors(KNN). The comparison results indicate that the proposed method outperforms other techniques and can be utilized as a promising tool for failures time series trend prediction.
In this paper, a family of arbitrarily high-order structure-preserving exponential Runge-Kutta methods is developed for the nonlinear Schrödinger equation by combing the scalar auxiliary variable approach and the...
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In visual relationship detection, human-notated relationships can be regarded as determinate relationships. However, there are still large amount of unlabeled data, such as object pairs with less significant relations...
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