Huabin Ruan

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The data-intensive scientific discoveries are generating huge amounts of data at an alarming rate. Most of the data are multidimensional and stored in array-based file formats. The processing of such big data becomes an urgent challenge. In this paper, we present SciHive, a scalable and easy-to-use array-based query system. SciHive enables scientists to(More)
State-of-the-art dimensional speech emotion recognition systems are trained using continuously labelled instances. The data labelling process is labour intensive and time-consuming. In this paper, we propose to apply active learning to reduce according efforts: The unlabelled instances are evaluated automatically, and only the most informative ones are(More)
Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning(More)
The classical Jacobi method is widely used for solving linear systems. This method is considerably timeconsuming to compute millions upon millions of linear equations. In this study, we design a novel FPGA-based Jacobi Solver. The kernel of the Jacobi Solver is a pipeline-friendly iteration algorithm which can eliminate the data dependence between iteration(More)
The financial market server in exchanges aims to maintain the order books and provide real time market data feeds to traders. Low-latency processing is in a great demand in financial trading. Although software solutions provide the flexibility to express algorithms in high-level programming models and to recompile quickly, it is becoming increasingly(More)
The Gaussian Copula Model (GCM) plays an important role in the state-of-the-art financial analysis field for modeling the dependence of financial assets. However, the existing implementations of GCM are all computationallydemanding and time-consuming. In this paper, we propose a Dataflow Engine (DFE) design to accelerate the GCM computation. Specifically, a(More)
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