The transient solution of time-dependent M/M/1 queues
The transient behavior of time-dependent M/M/1 queues is studied. The boundary probability function pi /sub 0/(t), which is the probability that the queue is empty at time t, is shown with analyticity arguments to satisfy a Volterra-type integral equation. The boundary integral equation is derived, and a numerical algorithm is used to solve the integral equation and to find the expected queue size from pi /sub 0/(t). The approach can be applied to many other types of time-dependent queues. Examples are given.

title: Performance study of Markov modulated fluid flow models with priority traffic
abstract: The steady-state performance of generic high-speed queuing system with priority traffic is studied, using the Markov modulated fluid models. the focus is on systems with traffic partitioned into two priorities according to a splitting factor alpha , so that low priority traffic can be discarded when congestion occurs. The results are used to further study issues of congestion control with priority traffic. In particular, the expected values, waiting times, variances of the buffer contents, and tail distributions are evaluated. It is found that the waiting time is more sensitive to changes of alpha than the buffer contents. This shows that for time-constrained traffic, the priority assignment is important for properly balancing the tradeoff between packet loss and delays. This model can be considered as a direct extension of the fluid-flow model to include priorities.<<ETX>>

title: A Simplified Learning Algorithm of Incremental Bayesian
abstract: A proportion factor is constructed though the Maximum Aposteriori Probability of examples in test data to select the training examples in incremental learning process. Instead of complex normal classify loss expression, the proportion factor λ is used to estimate the classify loss to improve classification efficiency. The final experiment shows that this algorithm is feasible, and more accurate than simple Bayesian classifier. The computing time is highly reduced on the optimal selection of examples in incremental learning process.

title: A new edge-based text verification approach for video
abstract: In this paper, we propose a new edge-based text verification approach for video. Based on the investigation of the relation between candidate blocks and their neighbor areas, the proposed approach first detects background edges in candidate blocks, then erases them by an edge tracking technique, and finally the candidate blocks containing too few remaining edges are eliminated as false alarms. Three measures for text detection evaluation in video were used to assess the performance of the proposed text verification approach. Experimental results on 50 broadcast news video clips demonstrate the validity of our approach.

title: A Case Study: A Model-Based Approach to Retrofit a Network Fault Management System with Self-Healing Functionality
abstract: Adding self-healing capabilities to network management systems holds great promise for delivering important goals, such as QoS, while simultaneously lowering capital expenditure, operation cost, and maintenance cost. In this paper, we present a model-based approach to add self-healing capabilities to a fault management system for cellular networks. We propose a generic modeling framework to categorize software failures and specify their dispositions at the model level for the target system. This facilitates the deployment of a control loop for adding autonomic capabilities into the system architecture, which include self-monitoring, self-healing, and self-adjusting functionality. While self- monitoring oversees the environmental conditions and system behavior, self-healing is accomplished by instrumenting the system with self-adjusting operations. We include a case study on a prototype intelligent network fault management system to illustrate this approach by showing how these autonomic capabilities can be added and deployed. Specifically, these autonomic capabilities are derived from self-model specifications, and are used to mitigate the risk of specified failures and maintain the health of the system in response to different types of faults encountered.

title: Increase of potential intellectual bandwidth in a scientific community through implementation of an end-user information system
abstract: Qureshi, et al. (2002) presented a case study where they used a framework, the intellectual bandwidth model to measure an organization's ability to create value. The model consists of two dimensions, information assimilation and collaboration. The IB model is a useful tool for managers because it enables them to plan solutions to complex business problems and to easily communicate these plans to key individuals. The model can also serve as a guide when making investment decisions. While it is important to increase an organization's intellectual bandwidth, it is equally important to assure that the resulting increase is employed to improve the organization's ability to innovate since, as it has been shown, innovation is the number one creator of organizational wealth (Baum et al., 2000). In this paper we discuss how intellectual bandwidth as proposed by Nunamaker et al. (2001) can be extended beyond the highest level of information assimilation identified in the model, automated sense-making, to ensure implementation of product innovations. To do this we propose adding two new information assimilation features, automated utilization and automated implementation. A real business example from the bioscience industry is presented in order to demonstrate how the IB model with extensions can be used. The study discusses an end-user information system, its current use and its potential to enhance an organization's value-creating capability.

title: A New Heuristic Reduct Algorithm Base on Rough Sets Theory
abstract: Real world data sets usually have many features, which increases the complexity of data mining task. Feature selection, as a preprocessing step to the data mining, has been shown very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving comprehensibility. To find the optimal feature subsets is the aim of feature selection. Rough sets theory provides a mathematical approach to find optimal feature subset, but this approach is time consuming. In this paper, we propose a novel heuristic algorithm based on rough sets theory to find out the feature subset. This algorithm employs appearing frequency of attribute as heuristic information. Experiment results show in most times our algorithm can find out optimal feature subset quickly and efficiently.