31582417
An Agent-Based Soft Computing Society
Soft computing (SC) techniques such as fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are complementary. Each SC technique has particular computational properties that make them suited for particular problems and not for others. Thus, in solving complex, real-world problems, we need to incorporate some SC techniques into the application systems to increase the systems' "intelligence". In this paper, we first propose an agent-based framework for integrating SC techniques into practical application systems. We then discuss the design and implementation of a platform independent soft computing support environment based on the framework. We call such an environment agent-based soft computing society. Such a society can facilitate the design of truly robust, flexible and adaptive hybrid intelligent systems.
40273501
Pattern Discovery in Probabilistic Databases
Modeling probabilistic data is one of important issues in databases due to the fact that data is often uncertainty in real-world applications. So, it is necessary to identify potentially useful patterns in probabilistic databases. Because probabilistic data in 1NF relations is redundant, previous mining techniques don't work well on probabilistic databases. For this reason, this paper proposes a new model for mining probabilistic databases. A partition is thus developed for preprocessing probabilistic data in a probabilistic databases. We evaluated the proposed technique, and the experimental results demonstrate that our approach is effective and efficient.
1498226
Approaches to implementing decision aggregation in multi-agent systems
10843699
Efficient feature extraction for 2D/3D objects in mesh representation
Meshes are dominantly used to represent 3D models as they fit well with graphics rendering hardware. Features such as volume, moments, and Fourier transform coefficients need to be calculated from the mesh representation efficiently. We propose an algorithm to calculate these features without transforming the mesh into other representations such as the volumetric representation. To calculate a feature for a mesh, we show that we can first compute it for each elementary shape such as a triangle or a tetrahedron, and then add up all the values for the mesh. The algorithm is simple and efficient, with many potential applications.
17966080
User Concept Pattern Discovery Using Relevance Feedback And Multiple Instance Learning For Content-Based Image Retrieval
Understanding and learning the subjective aspect of humans in Content-Based Image Retrieval has been an active research field during the past few years. However, how to effectively discover users' concept patterns when there are multiple visual features existing in the retrieval system still remains a big issue. In this paper, we propose a multimedia data mining framework that incorporates Multiple Instance Learning into the user relevance feedback in a seamless way to discover the concept patterns of users, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of users. This underlying mapping can be progressively discovered through the feedback and learning procedure. The role user plays in the retrieval system is to guide the system mining process to his/her own focus of attention. The retrieval performance is tested under a couple of conditions.
2587849
Quantifying aspects in middleware platforms
Middleware technologies such as Web Services, CORBA and DCOM have been very successful in solving distributed computing problems for a large family of application domains. As middleware systems are getting widely adopted and more functionally mature, it is also increasingly difficult for the architecture of middleware to achieve a high level of adaptability and configurability, due to the limitations of traditional software decomposition methods. Aspect oriented programming has brought us new design perspectives because it permits the superimpositions of multiple abstraction models on top of one another. It is a very powerful technique in separating and simplifying design concerns. In this paper, we first show that, through the quantification of aspects in the legacy implementations, the modularity of middleware architecture is greatly hindered by the ubiquitous existence of tangled logic. We then go one step further by factoring out a number of aspects identified in the mining work and re-implementing them as aspect programs. The aspect oriented re-factorization allows us to apply a set of software engineering metrics to quantify the changes of the re-factored system in both the structural complexity and the runtime performance. The aspect oriented re-factoring proves that the aspect oriented programming is capable of composing orthogonal design requirements. The final "woven" system is able to correctly provide both the fundamental functionality and the "aspectized" functionality with negligible overhead and a leaner architecture. Further more, the configurability of middleware is dramatically increased because the "aspectized" features can be configured in and out during the compile-time
13973220
Database-Driven Grid Computing with GridBASE
The GridBASE framework for database-driven grid computing is presented. The design and a prototype implementation of the framework is discussed. Industry-strength database technology plays a key role in the design of the framework. The database is used as a scalable, reliable and remotely accessible component both for storing and organizing the configuration information of the grid, and for managing information related to the grid users and the jobs and tasks they submit for execution. Other system components are worker nodes, a simple resource broker, a grid operator console, and application clients. In analogy with electrical power grids, a clear distinction is made in our design between the role played by grid users on the one hand, who develop and submit application code but are otherwise mostly isolated from resource deployment and selection, and the role played by the grid operator on the other hand, who is responsible for providing computing resources and assuring system availability and maintenance. Application code can be written in any language, and simple workflow support is provided. In our prototype implementation we experiment with code delivery and input and output file delivery via the database component. Our approach is based on decentralization and implemented in Java, leading to a lightweight, portable and scalable grid computing solution that is especially suited for parallel bioinformatics. Deployment of GridBASE on Ontario's SHARCNET and application to virtual experiments in RNA folding statistics are described.
7781593
Kernel based 2D symmetrical principal component analysis for face classification
This paper presents a novel algorithm-kernel based 2D symmetrical principal component analysis (K2DSPCA), which takes full advantage of kernel method, the symmetrical property of facial image and the structural information of image (i.e., the advantage of two-dimensional PCA). Firstly, a facial image is decomposed into an even image and an odd image; Secondly, both the even image and the odd image are mapped to a high dimensional feature space (Reproducing Kernel Hilbert space, RKHS) by a nonlinear function; Thirdly, compute the eigenvectors and the eigenvectors of the even image and the odd image in RKHS, respectively; At last, select the eigenvectors with greater variance as the projection axis. We compare the performance of SPCA, 2DPCA, S2DPCA with K2DSPCA on CBCL database for binary classification, and on ORL face database for multi-category classification, respectively. The experimental results show the K2DSPCA is competitive with or superior to SPCA, 2DPCA and S2DPCA.