Xiaodi Huang

Clustering graphs for visualization via node similarities

Graph visualization is commonly used to visually model relations in many areas. Examples include Web sites, CASE tools, and knowledge representation. When the amount of information in these graphs becomes too large, users, however, cannot perceive all elements at the same time. A clustered graph can greatly reduce visual complexity by temporarily replacing a set of nodes in clusters with abstract nodes. This paper proposes a new approach to clustering graphs. The approach constructs the node similarity matrix of a graph that is derived from a novel metric of node similarity. The linkage pattern of the graph is thus encoded into the similarity matrix, and then one obtains the hierarchical abstraction of densely linked subgraphs by applying the k-means algorithm to the matrix. A heuristic method is developed to overcome the inherent drawbacks of the k-means algorithm. For clustered graphs we present a multilevel multi-window approach to hierarchically drawing them in different abstract level views with the purpose of improving their readability. The proposed approaches demonstrate good results in our experiments. As application examples, visualization of part of Java class diagrams and Web graphs are provided. We also conducted usability experiments on our algorithm and approach. The results have shown that the hierarchically clustered graph used in our system can improve user performance for certain types of tasks.

Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus

More often than not, a multimedia data described by multiple features, such as color and shape features, can be naturally decomposed of multi-views. Since multi-views provide complementary information to each other, great endeavors have been dedicated by leveraging multiple views instead of a single view to achieve the better clustering performance. To effectively exploit data correlation consensus among multi-views, in this paper, we study subspace clustering for multi-view data while keeping individual views well encapsulated. For characterizing data correlations, we generate a similarity matrix in a way that high affinity values are assigned to data objects within the same subspace across views, while the correlations among data objects from distinct subspaces are minimized. Before generating this matrix, however, we should consider that multi-view data in practice might be corrupted by noise. The corrupted data will significantly downgrade clustering results. We first present a novel objective function coupled with an angular based regularizer. By minimizing this function, multiple sparse vectors are obtained for each data object as its multiple representations. In fact, these sparse vectors result from reaching data correlation consensus on all views. For tackling noise corruption, we present a sparsity-based approach that refines the angular-based data correlation. Using this approach, a more ideal data similarity matrix is generated for multi-view data. Spectral clustering is then applied to the similarity matrix to obtain the final subspace clustering. Extensive experiments have been conducted to validate the effectiveness of our proposed approach.

A new algorithm for removing node overlapping in graph visualization

Techniques for drawing graphs have proven successful in producing good layouts of undirected graphs. When nodes must be labeled however, the problem of overlapping nodes arises, particularly in dynamic graph visualization. Providing a formal description of this problem, this paper presents a new approach called the Force-Transfer algorithm that removes node overlaps. Compared to other methods, our algorithm is usually able to achieve a compact adjusted layout within a reasonable running time.

A Novel Approach for Adopting Cloud-Based E-learning System

The advent of cloud computing in recent years has sparked an interest from different organizations, institutions and users to take its advantage. This is a result of the new economic model for the Information Technology (IT) that cloud computing promises. It promises a shift from an organization required to invest heavily for limited IT resources that are internally managed, to a model where the organization can buy or rent resources that are managed by a cloud provider and pay per use. Although the adoption of cloud computing promises various benefits, a successful adoption of cloud computing in an educational institute still requires an understanding of different dynamics and expertise in diverse domains. Currently, there are inadequate guidelines for adopting cloud computing and building trust. Therefore, this paper presents a framework that specifies a number of steps for academic institutes as well as organizations to adopt cloud computing. The framework is designed by taking into account a range of strategic issues and technological factors from a broad cross section of areas of expertise in order to ensure a successful cloud computing adoption.

Enhanced clustering of biomedical documents using ensemble non-negative matrix factorization

Searching and mining biomedical literature databases are common ways of generating scientific hypotheses by biomedical researchers. Clustering can assist researchers to form hypotheses by seeking valuable information from grouped documents effectively. Although a large number of clustering algorithms are available, this paper attempts to answer the question as to which algorithm is best suited to accurately cluster biomedical documents. Non-negative matrix factorization (NMF) has been widely applied to clustering general text documents. However, the clustering results are sensitive to the initial values of the parameters of NMF. In order to overcome this drawback, we present the ensemble NMF for clustering biomedical documents in this paper. The performance of ensemble NMF was evaluated on numerous datasets generated from the TREC Genomics track dataset. With respect to most datasets, the experimental results have demonstrated that the ensemble NMF significantly outperforms classical clustering algorithms of bisecting K-means, and hierarchical clustering. We compared four different methods for constructing an ensemble NMF. For clustering biomedical documents, this research is the first to compare ensemble NMF with typical classical clustering algorithms, and validates ensemble NMF constructed from different graph-based ensemble algorithms. This is also the first work on ensemble NMF with Hybrid Bipartite Graph Formulation for clustering biomedical documents.

MHC2MIL: a novel multiple instance learning based method for MHC-II peptide binding prediction by considering peptide flanking region and residue positions

BackgroundComputational prediction of major histocompatibility complex class II (MHC-II) binding peptides can assist researchers in understanding the mechanism of immune systems and developing peptide based vaccines. Although many computational methods have been proposed, the performance of these methods are far from satisfactory. The difficulty of MHC-II peptide binding prediction comes mainly from the large length variation of binding peptides.MethodsWe develop a novel multiple instance learning based method called MHC2MIL, in order to predict MHC-II binding peptides. We deem each peptide in MHC2MIL as a bag, and some substrings of the peptide as the instances in the bag. Unlike previous multiple instance learning based methods that consider only instances of fixed length 9 (9 amino acids), MHC2MIL is able to deal with instances of both lengths of 9 and 11 (11 amino acids), simultaneously. As such, MHC2MIL incorporates important information in the peptide flanking region. For measuring the distances between different instances, furthermore, MHC2MIL explicitly highlights the amino acids in some important positions.ResultsExperimental results on a benchmark dataset have shown that, the performance of MHC2MIL is significantly improved by considering the instances of both 9 and 11 amino acids, as well as by emphasizing amino acids at key positions in the instance. The results are consistent with those reported in the literature on MHC-II peptide binding. In addition to five important positions (1, 4, 6, 7 and 9) for HLA(human leukocyte antigen, the name of MHC in Humans) DR peptide binding, we also find that position 2 may play some roles in the binding process. By using 5-fold cross validation on the benchmark dataset, MHC2MIL outperforms two state-of-the-art methods of MHC2SK and NN-align with being statistically significant, on 12 HLA DP and DQ molecules. In addition, it achieves comparable performance with MHC2SK and NN-align on 14 HLA DR molecules. MHC2MIL is freely available at http://datamining-iip.fudan.edu.cn/service/MHC2MIL/index.html.

Synthesizing mullite from beneficiated fly ash

In the United States, approximately 50 million tonnes of fly ash are generated by electrical utilities annually. The current consumption rate for fly ash materials is less than 20% because most fly ash materials do not meet market specifications and the quality of the ash is inconsistent. A beneficiation process that produces quality-controlled fly ash components has been developed. The synthesis of mullite as a refractory material is one industrial application for the silicate sphere component from the beneficiated fly ash. As-received (untreated) fly ash did not produce a usable mullite refractory. This article discusses the fly ash beneficiation process and mullite synthesis.

Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information

The interaction between stock price process and market news has been widely analyzed by investors on different markets. Previous works, however, focus either on market news purely as exogenous factors that tend to lead price process or on the analysis of how past stock price process can affect future stock returns. To take a step forward, we quantitatively integrate information from both market news and stock prices in order to improve the accuracy of prediction on stock future price return in an intra-day trading context. In this paper, we present the design and architecture of our approach for market information fusion. By means of multiple kernel learning, the hidden information behind the two sources is effectively extracted, and more importantly, seamlessly integrated rather than simply combined by a single kernel approach. Experiments on comprehensive comparisons between our approach and three baseline methods (which use only one type of information, or naively combine the two sources) have been conducted on the intra-day tick-by-tick data of the Hong Kong Stock Exchange and market news archives of the same period. It has been shown that for both cross-validation and independent testing, our approach is able to achieve the best results.

UsageQoS: Estimating the QoS of Web Services through Online User Communities

Services are an indispensable component in cloud computing. Web services are particularly important. As an increasing number of Web services provides equivalent functions, one common issue faced by users is the selection of the most appropriate one based on quality. This article presents a conceptual framework that characterizes the quality of Web services, an algorithm that quantifies them, and a system architecture that ranks Web services by using the proposed algorithm. In particular, the algorithm, called UsageQoS that computes the scores of quality of service (QoS) of Web services within a community, makes use of the usage frequencies of Web services. The frequencies are defined as the numbers of times invoked by other services in a given time period. The UsageQoS algorithm is able to optionally take user ratings as its initial input. The proposed approach has been validated by extensively experimenting on several datasets, including two real datasets. The results of the experiments have demonstrated that our approach is capable of estimating QoS parameters of Web services, regardless of whether user ratings are available or not.

Enhanced M-Learning with cloud computing: The Bangladesh case

M-Learning is a learning model that blends wireless technology and mobile computing to educate the world. It is convenient in that it is accessible from virtually anywhere. However, it faces the challenges such as the high cost and lack of sufficient education resources. This paper presents a new M-learning architecture that is enhanced by cloud computing. Relying on this architecture, we can reach the huge number of students over diverse terrains in a timely and cost effective manner, by using the mobile technology as a supplement to the existing learning technologies. A new learning framework for Bangladesh is also described that makes use of mobile phones combined with cloud computing to achieve cost-effectiveness.