A model-based approach to adding autonomic capabilities to network fault management system
Adding autonomic capabilities to network management systems provides great promise in delivering high QoS while lowering operation and maintenance cost. In this paper, we present a model-based approach to adding autonomic capabilities to a fault management system for cellular networks. We propose the use of modeling techniques to specify software failures and 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. Our case study on the intelligent network fault management system illustrates the proposed approach by adding and deploying these autonomic capabilities derived from self-model specifications, to mitigate the risk of specified failures and maintain the level of healthiness of the system, dynamically and effectively.

title: Enhancing Software Product Line Maintenance with Source Code Mining
abstract: Large-scale reuse and accelerated software development have been some of the key attractions behind software product lines. Various strategies and processes have been developed to facilitate product line development, maintenance, and evolution. However, experiences with software product lines also showed that it is a rather challenging task to maintain software product lines and families over a long period of time. The time and effort needed to manage and maintain product lines increase and quality degrades as product lines evolve. Without proper methods and tools to support the evolution, the cost can outweigh the benefits. 
 
This paper describes an approach to simplifying the maintenance of software product lines and improving software quality by integrating traditional software maintenance practices with pattern-based source code mining for defect detection and correction. Our case studies were performed in an industrial setting where the evolution of multiple mobile phone models of a product line was investigated.

title: A Context-Aware Policy Model to Support Autonomic Networking
abstract: Autonomic networking requires that the services and resources offered by the network at any specific time adapt to evolving changes in user needs, business goals and environmental conditions. This paper describes a rich and extensible context-aware policy model as part of the FOCALE autonomic network architecture. This context-aware policy model is built explicitly to support context-awareness that in turn enables the control loops in FOCALE to be adaptive.

title: Metamodel-driven model interpreter evolution
abstract: Domain-specific modeling (DSM) raises the level of abstraction by specifying a metamodel that is aligned to a particular problem domain. A key feature of DSM is the construction of model interpreters that synthesize the domain models into software artifacts. In the presence of new stakeholder requirements, it is possible that a metamodel undergoes numerous changes during periods of evolution (a new instance of the classic schema evolution problem). Consequently, there is a fundamental problem in keeping the model interpreters up to date with metamodel changes. This research abstract outlines the technical challenges in formalizing model interpreter implementation with the intent to facilitate interpreter evolution in terms of metamodel migration.

title: Spectral Decomposition Approach for Transient Analysis of Multi-Server Discrete-Time Queues
abstract: Discrete time queueing systems are extensively used in modeling the ATM environment at the cell level. This paper examines the transient behavior of a discrete time FCFS multiple server queue. For the arrival process we assume a general discrete time Markovian batch arrival process, where the batch size distribution of the arrivals in successive slots is governed by a finite state discrete time Markov chain. We show that once the spectral decomposition of the “probability generating matrix” of the arrival process is obtained, the complete solution in the transform domain may be given. Using the complex analysis technique and Cauchy's integral formula, we present an efficient numerical method for the numerical calculation of a few performance measures of interest, namely transient probability of queue being empty, and the mean of the queue length distribution. We also generalize our numerical method in the situations where the superposition of a number of independent arrival sources are fed to the queue. It is shown that the numerical complexity of obtaining the transient solution in this case can be substantially reduced by using the approach based on the Kronecker product. The finite buffer case is also analyzed.

title: Adaptive Training of a Kernel-Based Representative and Discriminative Nonlinear Classifier
abstract: Adaptive training of a classifier is necessary when feature selection and sparse representation are considered. Previously, we proposed a kernel-based nonlinear classifier for simultaneous representation and discrimination of pattern features. Its batch training has a closed-form solution. In this paper we implement an adaptive training algorithm using an incremental learning procedure that exactly retains the generalization ability of batch training. It naturally yields a sparse representation. The feasibility of the presented methods is illustrated by experimental results on handwritten digit classification.

title: A Dynamic Scalable Video Conference System Based on SIP
abstract: The Session Initiation Protocol (SIP) provides powerful and flexible signaling capabilities for building video conferencing services. Traditionally, for SIP-based centralized video conference systems, the conferencing scale is mainly limited by both the capability of conference server and the availability of bandwidth. In this paper, our design focuses on how to provide dynamic scalability for the SIP-based video conferencing system when the number of conference users increases continually. Based on the study of the SIP protocol and the existing video conferencing models, we propose a dynamic scalable service model that can support to dynamically increase the number of conference servers without negative influence on the stability of system. This enables the extra service requests to be transferred and served in the cooperated conference servers. The paper also addresses the SIP-enabled conferencing flows based on the model in detail. We developed a prototype of video conference system based on the proposed model. Experimental results demonstrate the validity of this service model.

title: A robust approach for recognition of text embedded in natural scenes
abstract: In this paper, we propose a robust approach for recognition of text embedded in natural scenes. Instead of using binary information as most other OCR systems do, we extract features from intensity of an image directly. We utilize a local intensity normalization method to effectively handle lighting variations. We then employ Gabor transform to obtain local features, and use the linear discriminant analysis (LDA) for selection and classification of features. The proposed method has been applied to a Chinese sign recognition task. The system can recognize a vocabulary of 3755 level I Chinese characters in the Chinese national standard character set GB2312-80 with various print fonts. We tested the system on 1630 test characters in sign images captured from the natural scenes, and the recognition accuracy was 92.46%. We have integrated the system into our automatic Chinese sign translation system.