Zhengding Lu

Community mining on dynamic weighted directed graphs

This paper focuses on community mining including community discovery and change-point detection on dynamic weighted directed graphs(DWDG). Real networks such as e-mail, co-author and financial networks can be modeled as DWDG. Community mining on DWDG has not been studied thoroughly, although that on static(or dynamic undirected unweighted)graphs has been exploited extensively. In this paper, Stream-Group is proposed to solve community mining on DWDG. For community discovery, a two-step approach is presented to discover the community structure of a weighted directed graph(WDG) in one time-slice: (1)The first step constructs compact communities according to each nodes single compactness which indicates the degree of a node belonging to a community in terms of the graphs relevance matrix; (2)The second step merges compact communities along the direction of maximum increment of the modularity. For change-point detection, a measure of the similarity between partitions is presented to determine whether a change-point appears along the time axis and an incremental algorithm is presented to update the partition of a graph segment when adding a new arriving graph into the graph segment. The effectiveness and efficiency of our algorithms are validated by experiments on both synthetic and real networks. Results show that our algorithms have a good trade-off between the effectiveness and efficiency in discovering communities and change-points.

Real-Time Compressed- Domain Video Watermarking Resistance to Geometric Distortions

A proposed real-time video watermarking scheme is transparent and robust to geometric distortions, including rotation with cropping, scaling, aspect ratio change, frame dropping, and swapping.

Incremental K-clique clustering in dynamic social networks

Clustering entities into dense parts is an important issue in social network analysis. Real social networks usually evolve over time and it remains a problem to efficiently cluster dynamic social networks. In this paper, a dynamic social network is modeled as an initial graph with an infinite change stream, called change stream model, which naturally eliminates the parameter setting problem of snapshot graph model. Based on the change stream model, the incremental version of a well known k-clique clustering problem is studied and incremental k-clique clustering algorithms are proposed based on local DFS (depth first search) forest updating technique. It is theoretically proved that the proposed algorithms outperform corresponding static ones and incremental spectral clustering algorithm in terms of time complexity. The practical performances of our algorithms are extensively evaluated and compared with the baseline algorithms on ENRON and DBLP datasets. Experimental results show that incremental k-clique clustering algorithms are much more efficient than corresponding static ones, and have no accumulating errors that incremental spectral clustering algorithm has and can capture the evolving details of the clusters that snapshot graph model based algorithms miss.

Role mining based on weights

Role mining from the existing permissions has been widely applied to aid the process of migrating to an RBAC system. While all permissions are treated evenly in previous approaches, none of the work has employed the weights of permissions in role mining to our knowledge, thus providing the motivation for this work. In this paper, we generalize this to the case where permissions are given weights to reflect their importance to the system. The weights can correspond to the property of operations, the sensitive degree of objects, and the attribute of users associated with permissions. To calculate the weight of permissions, we introduce the concept of similarity between both users and permissions, and use a similarity matrix to reinforce the similarity between permissions. Then we create a link between the reinforced similarity and the weight of permissions. We further propose a weighted role mining algorithm to generate roles based on weights. Experiments on performance study prove the superiority of the new algorithm.

Mining constraints in role-based access control

Abstract Constraints are an important aspect of role-based access control (RBAC) and sometimes argued to be the principal motivation of RBAC. While role engineering is proposed to define an architectural structure of the organization’s security policies, none of the work has employed constraint mining in migrating a non-RBAC system to an RBAC system to our knowledge, thus providing the motivation for this work. In this paper, we first define a wide variety of constraints, which are the best-known ones to date, and then create a relationship between the conventional data mining technology and the constraints. We further propose an anti-association rule mining algorithm to generate the constraints. Experiments on performance study prove the superiority of the new algorithm.

A Chinese word segmentation based on language situation in processing ambiguous words

While the processing of natural language is beneficial to the text mining. Chinese word segmentation is an important step in the processing of Chinese natural language. In this paper, the convergence essence of the segmentation process is analyzed, and a theory of Chinese word segmentation based on language situation is deducted. Based on the segmentation theory, an algorithm of Chinese word segmentation is presented. Both in theory and from the experiment results, the algorithm is efficient.

Robust video watermarking based on affine invariant regions in the compressed domain

This paper proposes a novel robust video watermarking scheme based on local affine invariant features in the compressed domain. This scheme is resilient to geometric distortions and quite suitable for DCT-encoded compressed video data because it performs directly in the block DCTs domain. In order to synchronize the watermark, we use local invariant feature points obtained through the Harris-Affine detector which is invariant to affine distortions. To decode the frames from DCT domain to the spatial domain as fast as possible, a fast inter-transformation between block DCTs and sub-block DCTs is employed and down-sampling frames in the spatial domain are obtained by replacing each sub-blocks DCT of 2x2 pixels with half of the corresponding DC coefficient. The above-mentioned strategy can significantly save computational cost in comparison with the conventional method which accomplishes the same task via inverse DCT (IDCT). The watermark detection is performed in spatial domain along with the decoded video playing. So it is not sensitive to the video format conversion. Experimental results demonstrate that the proposed scheme is transparent and robust to signal-processing attacks, geometric distortions including rotation, scaling, aspect ratio changes, linear geometric transforms, cropping and combinations of several attacks, frame dropping, and frame rate conversion.

WebPeer: A P2P-based system for publishing and discovering Web services

The use of Web services as an infrastructure of service sharing has made it possible to provide collaboration and interoperability in distributed computing environment. In this environment, service publishing and discovery are required as elementary functionalities for users to be able to locate the shared resources. The mechanism of service publishing and discovery with centralized architecture restricts the reliability and scalability of the distributed computing environment as the services and resources on the Web are fast emerging. The peer-to-peer (P2P) systems and applications, on the other hand, employ distributed resources to perform critical functions in a decentralized manner. This paper introduces Web Services Oriented Peer-to-peer (WSOP) architecture with a combination of centralized and decentralized characteristics, and presents a framework of service publishing and discovery model based on WSOP architecture. The prototype system - WebPeer implemented based on this model demonstrates the WSOP architecture can not only help to overcome the known obstacles in common Web Services infrastructure such as single node failure, but also extend the ability of the pure P2P systems, such as more efficiently locating the resources, increasing the interoperability between different P2P systems.

Robust image copy detection using multi-resolution histogram

This paper proposes a novel robust image copy detection scheme using multi-resolution histogram. In this method, the multi-resolution histogram, a global feature descriptor, is exploited to characterize an image. It shares many desirable properties with the plain color histogram including that they are both fast to compute, efficient for storage, invariant to rigid motions, and robust to noise. In addition, it encodes spatial information directly, which is essential to the discrimination of image signature for an image copy detection scheme. In order to verify the robustness of the proposed method, an intensive simulation has been performed. The experimental results demonstrate that our method is robust against both geometric distortions and signal-processing like attacks. P-R curves obtained under various attacks show that the performance of our method is better than such a few classical methods selected for comparison.

A novel image copy detection scheme based on the local multi-resolution histogram descriptor

The conventional research on the image copy detection concentrates on extracting features which are robust enough to resist various kinds of image attacks. However, the global features are sensitive to geometric attacks, especially cropping and rotation, while the local features cannot substantially represent the image spatial information and structure context. Instead of simply extracting feature from local region or global image directly, we propose a novel image copy detection scheme based on Scale Invariant Feature Transform (SIFT) detector and multi-resolution histogram descriptor (MHD). In this novel algorithm, a series of robust, homogenous and large size circular patches are firstly constructed using the SIFT detector, and then the MHD is introduced to generate a discriminative feature vector for each patch. Experimental results obtained from the benchmark attacks demonstrate that the performance of the proposed approach is better than existing methods, especially on the test against geometric distortions.