Research papers and code for "Qingguo Xu":
In this paper, we first define a difference measure between the old and new sequential patterns of stream data, which is proved to be a distance. Then we propose an experimental method, called TPD (Tradeoff between Performance and Difference), to decide when to update the sequential patterns of stream data by making a tradeoff between the performance of increasingly updating algorithms and the difference of sequential patterns. The experiments for the incremental updating algorithm IUS on two data sets show that generally, as the size of incremental windows grows, the values of the speedup and the values of the difference will decrease and increase respectively. It is also shown experimentally that the incremental ratio determined by the TPD method does not monotonically increase or decrease but changes in a range between 20 and 30 percentage for the IUS algorithm.

* 12 pages, 5 figures
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A head-mounted display (HMD) could be an important component of augmented reality system. However, as the upper face region is seriously occluded by the device, the user experience could be affected in applications such as telecommunication and multi-player video games. In this paper, we first present a novel experimental setup that consists of two near-infrared (NIR) cameras to point to the eye regions and one visible-light RGB camera to capture the visible face region. The main purpose of this paper is to synthesize realistic face images without occlusions based on the images captured by these cameras. To this end, we propose a novel synthesis framework that contains four modules: 3D head reconstruction, face alignment and tracking, face synthesis, and eye synthesis. In face synthesis, we propose a novel algorithm that can robustly align and track a personalized 3D head model given a face that is severely occluded by the HMD. In eye synthesis, in order to generate accurate eye movements and dynamic wrinkle variations around eye regions, we propose another novel algorithm to colorize the NIR eye images and further remove the "red eye" effects caused by the colorization. Results show that both hardware setup and system framework are robust to synthesize realistic face images in video sequences.

* 12 pages,15 figures
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In order to control the process of data mining and focus on the things of interest to us, many kinds of constraints have been added into the algorithms of data mining. However, discovering the correlated alarms in the alarm database needs deep domain constraints. Because the correlated alarms greatly depend on the logical and physical architecture of networks. Thus we use the network model as the constraints of algorithms, including Scope constraint, Inter-correlated constraint and Intra-correlated constraint, in our proposed algorithm called SMC (Search with Model-based Constraints). The experiments show that the SMC algorithm with Inter-correlated or Intra-correlated constraint is about two times faster than the algorithm with no constraints.

* the 9th IEEE International Conference on Telecommunications,June,2002, Beijing,China
* 8 pages, 7 figures
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Because the data being mined in the temporal database will evolve with time, many researchers have focused on the incremental mining of frequent sequences in temporal database. In this paper, we propose an algorithm called IUS, using the frequent and negative border sequences in the original database for incremental sequence mining. To deal with the case where some data need to be updated from the original database, we present an algorithm called DUS to maintain sequential patterns in the updated database. We also define the negative border sequence threshold: Min_nbd_supp to control the number of sequences in the negative border.

* The Second SIAM Data mining2002: workshop HPDM
* 12 pages, 4 figures
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Alarm correlation plays an important role in improving the service and reliability in modern telecommunications networks. Most previous research of alarm correlation didn't consider the effect of noise data in Database. This paper focuses on the method of discovering alarm correlation rules from database containing noise data. We firstly define two parameters Win_freq and Win_add as the measure of noise data and then present the Robust_search algorithm to solve the problem. At different size of Win_freq and Win_add, experiments with alarm data containing noise data show that the Robust_search Algorithm can discover the more rules with the bigger size of Win_add. We also experimentally compare two different interestingness measures of confidence and correlation.

* 15 pages,4 figures
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