17317140
A Reconfigurable High Availability Infrastructure in Cluster for Grid
The paper presents the implementation and analysis of a service-based reconfigurable High Availability infrastructure of cluster system for Grid. Based on service notion, the High Availability infrastructure is constructed for mission critical applications on Grid. The application high availability service is responsible for registered applications’ high availability. The Service Manager is in charge of monitoring all services via heartbeat and automatically recovering from failure to assure service high availability. To improve flexibility and dependability, a dynamic reconfiguration mechanism is designed so that the configurations can be changed without interrupting application’s running. A dependability modeling and simulation tool is deployed in the reconfiguration procedure. Dependability parameters can be inputted into the simulation tool. With the objects and sub-model library in the tool, corresponding global model will be constructed and statistic result for different recovery strategies is given. The configuration of application can be adjusted based on these statistic results.
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.
46501259
A proactive fault-detection mechanism in large-scale cluster systems
16252144
Trajectory based event tactics analysis in broadcast sports video
Most of existing approaches on event detection in sports video are general audience oriented. The extracted events are then presented to the audience without further analysis. However, professionals, such as soccer coaches, are more interested in the tactics used in the events. In this paper, we present a novel approach to extract tactic information from the goal event in broadcast soccer video and present the goal event in a tactic mode to the coaches and sports professionals. We first extract goal events with far-view shots based on analysis and alignment of web-casting text and broadcast video. For a detected goal event, we employ a multi-object detection and tracking algorithm to obtain the players and ball trajectories in the shot. Compared with existing work, we proposed an effective tactic representation called aggregate trajectory which is constructed based on multiple trajectories using a novel analysis of temporal-spatial interaction among the players and the ball. The interactive relationship with play region information and hypothesis testing for trajectory temporal-spatial distribution are exploited to analyze the tactic patterns in a hierarchical coarse-to-fine framework. The experimental results on the data of FIFA World Cup 2006 are promising and demonstrate our approach is effective.
6570274
Performance-Complexity Analysis of High Resolution Video Encoder and its Memory Organization for DSP Implementation
This paper first analyses the relationship between performance and complexity of several state-of-the-art coding algorithms for high resolution videos. Based on the coding efficiency comparison under different config parameters and the intra mode usage in P/B frame, this paper presents a practical scheme to improve the coding speed with slight quality loss. And a DSP-oriented two-level internal memory organization is also proposed to keep pipeline processing. In such organization, block correlation caused by motion vector predictions is lightened while keeping almost the same performance as the original
6277479
Software Pipelines Design for Variable Block-Size Motion Estimation with Large Search Range
This paper presents some techniques for efficient motion estimation (ME) implementation on fixed-point digital signal processor (DSP) for high resolution video coding. First, challenges in large search-range ME are discussed. Then, based on the statistics analysis of the best matched block, correlations in variable block-size ME are removed on algorithm level while keeping almost the same performance as the original fast search strategy. Based on this modification and data reuse technique, this paper proposes an "index search" method for DSP implementation, which is a good solution to balance both high coding efficiency and high coding speed on the DSP platform. In "index search", ME is divided into three steps and highly parallel pipelines are designed for each step. The fully use of pipelines brings about 85% time reduction during ME. "Index search" can also be easily extended to achieve different search range and search patterns.
17613283
Object detection using spatial histogram features
In this paper, we propose an object detection approach using spatial histogram features. As spatial histograms consist of marginal distributions of an image over local patches, they can preserve texture and shape information of an object simultaneously. We employ Fisher criterion and mutual information to measure discriminability and features correlation of spatial histogram features. We further train a hierarchical classifier by combining cascade histogram matching and support vector machine. The cascade histogram matching is trained via automatically selected discriminative features. A forward sequential selection method is presented to construct uncorrelated and discriminative feature sets for support vector machine classification. We evaluate the proposed approach on two different kinds of objects: car and video text. Experimental results show that the proposed approach is efficient and robust in object detection.
3877233
Quantitatively evaluating the influence of online social interactions in the community-assisted digital library
Online social interactions are useful in information seekingfrom digital libraries, but how to measure their influence on theuser's information access actions has not yet been revealed.Studies on this problem give us interesting insights into theworkings of human dynamics in the context of information accessfrom digital libraries. On the basis, we wish to improve thetechnological supports to provide more intelligent services in theongoing China-America Million Books Digital Library so that it canreach its potential in serving human needs.
Our research aims at developing a common framework to modelonline social interaction process in community-assisted digitallibraries. The underlying philosophy of our work is that the onlinesocial interaction can be viewed as a dynamic process, and the nextstate of each participant in this process (e.g., personalinformation access competency) depends on the value of the previousstates of all participants involving interactions in the period.Hence, considering the dynamics of interaction process, we modeleach participant with a Hidden Markov Model (HMM) chain and thenemploy <i>the Influence Model</i>, which was developed by C.Asavathiratham as a Dynamic Bayes Net (DBN) of representing theinfluences a number of Markov chains have on each other, to analyzethe effects of participants influencing each other. Therefore, onecan think of the entire interaction process as a DBN frameworkhaving two levels of structure: the local level and the networklevel. Each participant <i>i</i> has a local HMM chain&Ggr;(<i>A</i>) which characterizes the transition of hisinternal states in the interaction process with state-transitionprobability∑over<i>j</i><i>d<sub>ij</sub>P</i>(<i>S<sup>i</sup><sub>t</sub></i>|<i>S<sup>j</sup><sub>t-1</sub></i>) (Here <i>states</i> are hispersonal information access competence in different periods, whileobservations are his information access actions). Meanwhile, thenetwork level, which is described by a <i>network graph</i>&Ggr;(<i>D<sup>T</sup></i>) where<i>D</i>={<i>d<sub>ij</sub></i>} is the <i>influence factormatrix</i>, represents the interacting relations betweenparticipants. The strength of each connection,<i>d<sub>ij</sub></i>, describes the <i>influence factor</i> of theparticipant <i>j</i> at its begin on the one <i>i</i> at its end.Hence, this model describes the dynamic inter-influence process ofthe internal states of all participants involving onlineinteractions.
To automatically build the model, we need firstly to extractobserved features from the data of online social interactions andinformation access actions. Obviously, the effects of interactionsare stronger if messages are exchanged more frequently, or theparticipants access more information in the online digitallibraries during the period of time. Based on this consideration,we select the <i>interaction measure</i><i>IM<sup>i,j</sup><sub>t</sub></i> and the <i>amount ofinformation</i> <i>IA<sup>j</sup><sub>t</sub></i> as the estimationfeatures of <i>x<sup>i</sup><sub>t</sub></i>. The interactionmeasure <i>IA<sup>i</sup><sub>t</sub></i> and the amount ofinformation parameterize the features calculated automatically fromthe data of online social interactions between the participants<i>i</i> and <i>j</i>, and the features calculated from the data ofinformation access actions respectively. Secondly, we need todevelop a mechanism for learning the parameters<i>d<sub>ij</sub></i> and<i>P</i>(<i>S<sup>i</sup><sub>t</sub></i>|<i>S<sup>j</sup><sub>t-1</sub></i>.Given sequences of observations {<i>x<sup>i</sup><sub>t</sub></i>}for each chain <i>i</i>, we may easily utilize theExpectation-Maximization algorithm or the gradient-based learningalgorithm to get their estimation equations.
We ran our experiments in the online digital library of W3CConsortium (www.w3c.org), which contains a mass of news, electronicpapers or other materials related to web technologies. Users mayaccess and download any information and materials in this digitallibrary, and also may free discuss on any related technologicalproblems by means of its mailing lists. Six users were selected inour experiments to collaboratively perform paper -gathering tasksrelated to four given topics. Any user might call for help from theothers through the mailing lists when had difficulties in thisprocess. All participants were required to record subjectiveevaluations of the effects that the others influenced his tasks.Each experiment was scheduled by ten phases. And in each phase, wesampled <i>IM<sup>i,j</sup><sub>t</sub></i> and<i>IA<sup>i</sup><sub>t</sub></i> for each participant and then fedthem into the learning algorithms to automatically build theinfluence model. By comparing with the subjective influence graphs,the experimental results show that the influence model can estimateapproximately the influences of online social interactions.