Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation
Alarm correlation for fault management in large telecommunication networks demands scalable and reliable algorithms. In this paper, we propose a clustering based alarm correlation approach using sequential proximity between alarms. We define two novel distance metrics appropriate for measuring similarity between alarm sequences obtained from interval-based division: 1) the two-digit binary metric that values the occurrences of two alarms in neighboring intervals to tolerate the false separation of alarms due to interval-based alarm sequence division, and 2) the sequential ordering-based distance metric that considers the time of arrival for different alarms within the same interval. We validate both metrics by applying them with hierarchical clustering using real-world cellular network alarm data. The efficacy of the proposed sequential proximity based alarm clustering is demonstrated through a comparative study with existing similarity metrics.

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: Model-Driven Adaptive Self-healing for Autonomic Computing
abstract: Self-healing is a vital property that an autonomic system must possess in order to provide robust performance and survivability. The promise of self-healing depends on other properties that the system should provide, which include self-monitoring and self-configuring. Autonomic systems further require self-healing behavior to adapt to changes in user needs, business goals, and environmental conditions such that self-healing decisions are made dynamically and adaptively according to the system context. In this paper, we propose a model-driven approach that leverages modeling techniques, reliability engineering methodologies, and aspect-oriented development to realize an adaptive self- healing paradigm for autonomic computing.

title: Maintaining software product lines — an industrial practice
abstract: Improved product quality and accelerated software development through systematic reuse of common software assets have been the key attractions behind software product lines. Magnitude of improvement in industrial practices has been reported in the literature. However, experiences with software product line also showed that it is a rather chall enging task to maintain software product lines and families over a long period of time. The time and resources needed to manage and maintain product lines increase and quality degrades as product lines evolve. This paper describes an industrial practice of software product line maintenance and evolution. The conventional software maintenance process is enhanced with data mining techniques to uncover lost reuse pattern and defects, maintain reuse, and reduce design erosion of product lines. Case studies with mobile phone product lines are described.

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: A model-based approach to adding autonomic capabilities to network fault management system
abstract: 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: A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search
abstract: In this paper, we propose a unified topic modeling approach and its integration into the random walk framework for academic search. Specifically, we present a topic model for simultaneously modeling papers, authors, and publication venues. We combine the proposed topic model into the random walk framework. Experimental results show that our proposed approach for academic search significantly outperforms the baseline methods of using BM25 and language model, and those of using the existing topic models (including pLSI, LDA, and the AT model).

title: Name Disambiguation Using Atomic Clusters
abstract: Name ambiguity is a critical problem in many applications, in particular in the online bibliography systems, such as DBLP and CiteSeer. Previously, several clustering based methods have been proposed although, the problem still presents to be a big challenge for both research and industry communities. In this paper, we present a complementary study to the problem from another point of view. We propose an approach of finding atomic clusters to improve the performance of existing clustering-based methods. We conducted experiments on a dataset from a real-world system: Arnetminer.org. Experiments results show that significant improvements can be obtained by using the proposed atomic clusters finding approach (about +8% and +27% improvements depending on different clustering methods).

title: Factor analysis based anomaly detection
abstract: We propose a novel anomaly detection algorithm based on factor analysis and Mahalanobis distance. Factor analysis is used to uncover the latent structure (dimensions) of a set of variables. It reduces attribute space from a larger number of variables to a smaller number of factors. The Mahalanobis distance is used to determine the "similarity" of a set of values from an "unknown" sample to a set of values measured from a collection of "known" samples. Combined with factor analysis, Mahalanobis distance is extended to examine whether a given vector is an outlier from a model identified by "factors" based on factor analysis. We present a factor analysis-based network anomaly detection algorithm and apply it to DARPA intrusion detection evaluation data. The experimental results show that the proposed algorithm is able to detect network intrusions with relatively low false alarms.

title: Biomechanical Modeling of Bone-Needle Interaction for Haptic Rendering in Needle Insertion Simulation
abstract: Medical simulators are increasingly being used for surgical training. For interactive surgical simulation involving haptic rendering, the force at the needle tip has to be computed very fast. We are developing biomechanical models for bone needle insertion. The cortical bone can be regarded as a dense form of cancellous bone that can be modeled using a linear elastic material. The porosity of the bone determines the resistance felt as the user inserts the needle into the bone. The bar element method that represents each trabecular bone as a FE beam is most computationally efficient. With 1000 FE elements, the computed force feedback were close to the insertion force measured during experiments. However, the extended bar element method may be the more appropriate choice for taking into consideration the trabecular distribution and hence, inhomogeneous of bone. The simulation studies on bone-needle interaction also showed that a diamond bevel needle may penetrate the bone with less force

title: A Computational Framework and Browser for Supporting Automatic Genome Annotation
abstract: We present here a flexible computational framework to complete the large-scale computing tasks involved in automatic annotation of whole-genomes. The characteristics of this framework include a two-level job load system and NFS-based distributed store of replicated data. In addition, the storage structure of annotation results in a relational database system and a Web interface for graphical interactive browsing and searching on the data are also described. The framework has been used to identify a core set of human protein coding genes that are consistently annotated and of high quality, which can be accessed by the browser provided at http://bioinfo.hust.edu.cn/en/database/ben