46501259
A proactive fault-detection mechanism in large-scale cluster systems
39810254
NCPN: A Simulation Tool for Coloured Petri Nets
206840947
AppManager: a powerful service-based application management system for clusters
Cluster application management plays an important role in cluster systems. With more and more applications being settled on clusters, application management faces more and more challenging requirements. This paper introduces a service-based cluster application management system called AppManager. Different from traditional job management systems, AppManager has a service-based architecture. The components of a job management system are modularized and encapsulated into services. All these services are accessible within a cluster or outsides over Internet. Besides the basic functions of a traditional application management system, such as resource management, application schedule and control, AppManager implements many excellent features including supporting a wide variety of application types, platform flexibility, high system scalability, high availability assurance for application, scheduling algorithm extensibility and so on.
5790149
Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution
In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition, and propose a novel mis-alignment learning solution. Mis-alignment problem is firstly empirically investigated through systematically evaluating Fisherface's sensitivity to mis-alignment on the FERET face database by perturbing the eye coordinates, which reveals that the imprecise localization of the facial landmarks abruptly degenerates the Fisherface system. We explicitly define this problem as curse of mis-alignment to highlight its graveness. We then analyze the sources of curse of mis-alignment and group the possible solutions into three categories: invariant features, mis-alignment modeling, and alignment retuning. And then we propose a set of measurement combining the recognition rate with the alignment error distribution to evaluate the overall performance of specific face recognition approach with its robustness against the mis-alignment considered. Finally, a novel mis-alignment learning method, named E-Fisherface, is proposed to reinforce the recognizer to model the mis-alignment variations. Experimental results have impressively indicated the effectiveness of the proposed E-Fisherface in tackling the curse of mis-alignment problem.
553389
Highlight Ranking for Racquet Sports Video in User Attention Subspaces Based on Relevance Feedback
In this paper, we propose a method to rank the highlights of broadcast racquet sports videos. Compared with previous work, we integrate relevance feedback into highlight ranking framework to effectively capture the user's interest in attention subspaces and generate personalized ranking result. First, we establish three user attention subspaces and extract audio, visual, temporal affective features to represent the human perception of highlight in each subspace. Then, the highlight ranking models are constructed using support vector regression (SVR) for the three subspaces respectively. Finally, the three submodels are linearly combined to generate the final ranking model. Relevance feedback technique is employed to adjust the weights of each submodel to obtain the result which is suitable to the user's preference. Experimental results demonstrate our approach is effective.
26977504
An End-to-End Application System of AVS: AVS-IPTV in China
The AVS video coding standard is established by China. However it is not limited to be a national standard but an open standard. In September 2006, AVS was adopted as a candidate for IPTV standards by the ITU. AVS is becoming more and more well known by the world. Many related products and systems have been released in the past years. This paper will introduce an endtoend application system of AVS in China--AVS-IPTV. The AVS-IPTV system is built by CNC (China Netcom Group), which is a leading broadband communications and fixed-line telecommunications operator in China. The AVS-IPTV system proves AVS can meet the application requirements well.
6014433
Local Linear Regression (LLR) for Pose Invariant Face Recognition
The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is well known as one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given non-frontal view to obtain a virtual gallery/probe face. By formulating this kind of solutions as a prediction problem, this paper proposes a simple but efficient novel local linear regression (LLR) method, which can generate the virtual frontal view from a given non-frontal face image. The proposed LLR inspires from the observation that the corresponding local facial regions of the frontal and non-frontal view pair satisfy linear assumption much better than the whole face region. This can be explained easily by the fact that a 3D face shape is composed of many local planar surfaces, which satisfy naturally linear model under imaging projection. In LLR, we simply partition the whole non-frontal face image into multiple local patches and apply linear regression to each patch for the prediction of its virtual frontal patch. Comparing with other methods, the experimental results on CMU PIE database show distinct advantage of the proposed method
8724139
News Content Highlight via Fast Caption Text Detection on Compressed Video
Captions present in video frames play an important role in understanding video content. This paper presents a fast algorithm to automatically detect captions in MPEG compressed video. It is based on statistics features of caption text's chrominance components. The paper also discusses its principle and speed-up mechanism in detail. We have successfully exploited the technique to automatically construct the pictorial catalogue, a new content representation. Experiment results show the proposed caption detection algorithm has not only the ideal accuracy 96.6% and recall 100%, but also a detection speed of faster than real time.