BNU HKBU United International College

Zhuhai, China

BNU HKBU United International College

Zhuhai, China
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Wan C.-W.,Hong Kong Polytechnic University | Wong C.N.-Y.,Hong Kong Polytechnic University | Pin W.-K.,Hong Kong Polytechnic University | Wong M.H.-Y.,Hong Kong Polytechnic University | And 6 more authors.
Phytotherapy Research | Year: 2013

Hypercholesterolemia is a major risk factor for the development of cardiovascular disease and nonalcoholic fatty liver disease. Natural compounds have been proved to be useful in lowering serum cholesterol to slow down the progression of cardiovascular disease and nonalcoholic fatty liver disease. In the present study, the hypocholesterolemic and hepatoprotective effects of the dietary consumption of chlorogenic acid were investigated by monitoring plasma lipid profile (total cholesterol, triglycerides, high-density lipoprotein and low-density lipoprotein) in Sprague-Dawley rats fed with a normal diet, a high-cholesterol diet or a high-cholesterol diet supplemented with chlorogenic acid (1 or 10 mg/kg/day p.o.) for 28 days. Chlorogenic acid markedly altered the increased plasma total cholesterol and low-density lipoprotein but decreased high-density lipoprotein induced by a hypercholesterolemic diet with a dose-dependent improvement on both atherogenic index and cardiac risk factor. Lipid depositions in liver were attenuated significantly in hypercholesterolemic animals supplemented with chlorogenic acid. It is postulated that hypocholesterolemic effect is the primary beneficial effect given by chlorogenic acid, which leads to other secondary beneficial effects such as atheroscleroprotective, cardioprotective and hepatoprotective functions. The hypocholesterolemic functions of chlorogenic acid are probably due to the increase in fatty acids unitization in liver via the up-regulation of peroxisome proliferation-activated receptor α mRNA. Copyright © 2012 John Wiley & Sons, Ltd.

Chen T.Y.,Swinburne University of Technology | Kuo F.-C.,Swinburne University of Technology | Towey D.,BNU HKBU United International College | Zhou Z.Q.,University of Wollongong
Proceedings - International Conference on Quality Software | Year: 2012

In software testing, an oracle refers to a mechanism against which testers can decide whether or not outcomes of test case executions are correct. The oracle problem refers to situations when either an oracle is not available, or it is too expensive to apply. Metamorphic testing has emerged as an effective and efficient approach to alleviating the oracle problem. This article introduces the basic concepts and procedures of metamorphic testing, and gives examples to show its applications, and integration with other methods. © 2012 IEEE.

Su W.,BNU HKBU United International College | Wang J.,City University of Hong Kong | Lochovsky F.H.,Hong Kong University of Science and Technology | Liu Y.,Tsinghua National Laboratory for Information Sciences and Technology
IEEE Transactions on Knowledge and Data Engineering | Year: 2012

Web databases generate query result pages based on a user's query. Automatically extracting the data from these query result pages is very important for many applications, such as data integration, which need to cooperate with multiple web databases. We present a novel data extraction and alignment method called CTVS that combines both tag and value similarity. CTVS automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the QRRs are not contiguous, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs. We also design a new record alignment algorithm that aligns the attributes in a record, first pairwise and then holistically, by combining the tag and data value similarity information. Experimental results show that CTVS achieves high precision and outperforms existing state-of-the-art data extraction methods. © 2012 IEEE.

Benzi M.,Emory University | Ng M.,Hong Kong Baptist University | Niu Q.,BNU HKBU United International College | Niu Q.,Xi'an Jiaotong - Liverpool University | Wang Z.,Emory University
Journal of Computational Physics | Year: 2011

In this paper we introduce a Relaxed Dimensional Factorization (RDF) preconditioner for saddle point problems. Properties of the preconditioned matrix are analyzed and compared with those of the closely related Dimensional Splitting (DS) preconditioner recently introduced by Benzi and Guo [7]. Numerical results for a variety of finite element discretizations of both steady and unsteady incompressible flow problems indicate very good behavior of the RDF preconditioner with respect to both mesh size and viscosity. © 2011 Elsevier Inc.

Chen W.,BNU HKBU United International College
Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011 | Year: 2011

Discovering underlying communities in networks is an important task in network analysis. In the last decade, a large variety of algorithms have been proposed. However, most of them require global information or a centralized control. Those algorithms are infeasible in large-scale real networks due to computation and accessibility. In this paper, we propose a novel decentralized community detection algorithm based on information diffusion. We believe information diffusion in human society can allow us to understand the emergence of community structure. Being able to find out some critical nodes which play an important role in the formation of a community is an important byproduct for our algorithm. Experiments on various networks, including benchmark networks and synthetic networks, show that it is comparable to three decentralized algorithms and two representative centralized algorithms, in terms of stability and accuracy. © 2011 IEEE.

Chan J.Y.-Y.,Hong Kong Polytechnic University | Yuen A.C.-Y.,State Key Laboratory of Chinese Medicine and Molecular Pharmacology | Chan R.Y.-K.,BNU HKBU United International College | Chan S.-W.,Hong Kong Polytechnic University | Chan S.-W.,State Key Laboratory of Chinese Medicine and Molecular Pharmacology
Phytotherapy Research | Year: 2013

Cardiovascular disease (CVD) is a category of chronic noncommunicable diseases causing high global mortality and has been a heavy social burden in many countries. In the search of chemicals that arise from natural food source, allicin is one such ingredient from garlic that was discovered with the potential to provide beneficial effects to the cardiovascular system. From the pharmacokinetic studies, allicin is known to be hydrophobic and can be readily absorbed through the cell membrane without inducing any damage to the phospholipid bilayer and then rapidly metabolized to exert pharmacological effects that are important to the cardiovascular system. It was found to provide cardio-protective effects by inducing vasorelaxation and alleviating various pathological conditions of CVD, including cardiac hypertrophy, angiogenesis, platelet aggregation, hyperlipidemia and hyperglycemia. Allicin was also discovered to further protect the cardiovascular system by enhancing the antioxidant status by lowering the level of reactive oxygen species and stimulating the production of glutathione. Other pharmacological benefits such as anticancer and antimicrobial activities were also discussed. It is concluded that allicin can be potentially developed into a health product for the cardiovascular system. Copyright © 2012 John Wiley & Sons, Ltd.

Wang Q.,Sun Yat Sen University | Yuen P.C.,Hong Kong Baptist University | Yuen P.C.,BNU HKBU United International College | Feng G.,Sun Yat Sen University
Pattern Recognition | Year: 2013

Learning an appropriate distance metric is a critical problem in pattern recognition. This paper addresses the problem of semi-supervised metric learning. We propose a new regularized semi-supervised metric learning (RSSML) method using local topology and triplet constraints. Our regularizer is designed and developed based on local topology, which is represented by local neighbors from the local smoothness, cluster (low density) and manifold information point of view. The regularizer is then combined with the large margin hinge loss on the triplet constraints. In other words, we keep a large margin between different labeled samples, and in the meanwhile, we use the unlabeled samples to regularize it. Then the semi-supervised metric learning method is developed. We have performed experiments on classification using publicly available databases to evaluate the proposed method. To our best knowledge, this is the only method satisfying all the three semi-supervised assumptions, namely smoothness, cluster (low density) and manifold. Experimental results have shown that the proposed method outperforms state-of-the-art semi-supervised distance metric learning algorithms. © 2013 Elsevier Ltd. All rights reserved.

Wang T.,University of Hong Kong | Towey D.,BNU HKBU United International College
Proceedings of IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2012 | Year: 2012

A fundamental change in the way education is being delivered is taking place. Using Information and Communication Technology (ICT) in education has shifted from being an "add-on" to becoming an essential component of the curriculum. Recently, initiatives aimed at reducing the digital divide, and lightening the load of students' backpacks, such as the "e-schoolbag" project launched in Shanghai and other major cities, have reignited the discussion on ICT's increasing role in the classroom. This paper traces some of the background of "e-schoolbag" and other similar projects, and discusses the trends and expected benefits now being identified. The paper also reports on a preliminary investigation conducted in an international school in Hong Kong, where the penetration and adoption of ICT in the classroom is reported to be comparatively high, and where they have introduced Apple's iPad and Amazon's Kindle as core tools for classroom use. The paper looks at some of the challenges facing adoption of such tools, including the difficulties of obtaining electronic versions of the desired course content or textbooks, and reports on some of the solutions appearing for these problems. The paper concludes with some suggestions of future directions of research and development to support the incorporation of digital learning devices in the classroom. © 2012 IEEE.

Ma A.J.,Hong Kong Baptist University | Yuen P.C.,Hong Kong Baptist University | Yuen P.C.,BNU HKBU United International College | Lai J.-H.,Sun Yat Sen University | Lai J.-H.,Guangdong Province Key Laboratory of Information Security
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2013

This paper addresses the independent assumption issue in fusion process. In the last decade, dependency modeling techniques were developed under a specific distribution of classifiers or by estimating the joint distribution of the posteriors. This paper proposes a new framework to model the dependency between features without any assumption on feature/classifier distribution, and overcomes the difficulty in estimating the high-dimensional joint density. In this paper, we prove that feature dependency can be modeled by a linear combination of the posterior probabilities under some mild assumptions. Based on the linear combination property, two methods, namely, Linear Classifier Dependency Modeling (LCDM) and Linear Feature Dependency Modeling (LFDM), are derived and developed for dependency modeling in classifier level and feature level, respectively. The optimal models for LCDM and LFDM are learned by maximizing the margin between the genuine and imposter posterior probabilities. Both synthetic data and real datasets are used for experiments. Experimental results show that LCDM and LFDM with dependency modeling outperform existing classifier level and feature level combination methods under nonnormal distributions and on four real databases, respectively. Comparing the classifier level and feature level fusion methods, LFDM gives the best performance. © 1979-2012 IEEE.

Ma A.J.,Hong Kong Baptist University | Yuen P.C.,Hong Kong Baptist University | Yuen P.C.,BNU HKBU United International College
International Journal of Computer Vision | Year: 2014

This paper addresses the robustness issue of information fusion for visual recognition. Analyzing limitations in existing fusion methods, we discover two key factors affecting the performance and robustness of a fusion model under different data distributions, namely (1) data dependency and (2) fusion assumption on posterior distribution. Considering these two factors, we develop a new framework to model dependency based on probabilistic properties of posteriors without any assumption on the data distribution. Making use of the range characteristics of posteriors, the fusion model is formulated as an analytic function multiplied by a constant with respect to the class label. With the analytic fusion model, we give an equivalent condition to the independent assumption and derive the dependency model from the marginal distribution property. Since the number of terms in the dependency model increases exponentially, the Reduced Analytic Dependency Model (RADM) is proposed based on the convergent property of analytic function. Finally, the optimal coefficients in the RADM are learned by incorporating label information from training data to minimize the empirical classification error under regularized least square criterion, which ensures the discriminative power. Experimental results from robust non-parametric statistical tests show that the proposed RADM method statistically significantly outperforms eight state-of-the-art score-level fusion methods on eight image/video datasets for different tasks of digit, flower, face, human action, object, and consumer video recognition. © 2014 Springer Science+Business Media New York.

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