3771585
Icon-based visualization of large high-dimensional datasets
High dimensional data visualization is critical to data analysts since it gives a direct view of original data. We present a method to visualize large amount of high dimensional data. We divide dimensions of data into several groups. Then, we use one icon to represent each group, and associate visual properties of each icon with dimensions in each group. A high dimensional data record will be represented by multiple different types of icons located in the same position. Furthermore, we use summary icons to display local details of viewer's interests and the whole data set at meantime. We show its effectiveness and efficiency through a case study on a real large data set.
2350863
Generating and Applying Rules for Interval Valued Fuzzy Observations
One of the objectives of intelligent data engineering and automated learning is to develop algorithms that learn the environment, generate rules, and take possible courses of actions. In this paper, we report our work on how to generate and apply such rules with a rule matrix model. Since the environments can be interval valued and rules often fuzzy, we further study how to obtain and apply rules for interval valued fuzzy observations.
16132997
Manifold of facial expression
We propose the concept of manifold of facial expression based on the observation that images of a subject's facial expressions define a smooth manifold in the high dimensional image space. Such a manifold representation can provide a unified framework for facial expression analysis. We first apply active wavelet networks (AWN) on the image sequences for facial feature localization. To learn the structure of the manifold in the feature space derived by AWN, we investigated two types of embeddings from a high dimensional space to a low dimensional space: locally linear embedding (LLE) and Lipschitz embedding. Our experiments show that LLE is suitable for visualizing expression manifolds. After applying Lipschitz embedding, the expression manifold can be approximately considered as a super-spherical surface in the embedding space. For manifolds derived from different subjects, we propose a nonlinear alignment algorithm that keeps the semantic similarity of facial expression from different subjects on one generalized manifold. We also show that nonlinear alignment outperforms linear alignment in expression classification.
20310851
A mobile agent-based active network architecture for intelligent network control
The innovation and evolution of network technologies and services to enhance network functionality have traditionally encountered the problems of backward-compatibility and standardization. To resolve the related issues, active networks have been introduced to enable specific customization of network functionality without the lengthy standard-mediated committee processes. In this paper, we exploit the capability of mobile agent technology to construct an active network architecture. We argue that the mobile agent can be a building block for specific network customizations, and introduction of new network functionality. Both integrated and discrete operational models of network customizations can be supported in our prototype implementation. For the service development and deployment, an abstract service structure and a service loading mechanism are presented. Furthermore, we provide an agent management/control mechanism and devise a service management/control mechanism, which is either not available or incomplete in the other related work. The proposed architecture allows new service introduction, and as a result, achieves improved network functionality.
13451430
Non-negative matrix factorization framework for face recognition
Non-negative Matrix Factorization (NMF) is a part-based image representation method which adds a non-negativity constraint to matrix factorization. NMF is compatible with the intuitive notion of combining parts to form a whole face. In this paper, we propose a framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features and good recognition results. Based on the framework, we present two novel subspace methods: Fisher Non-negative Matrix Factorization (FNMF) and PCA Non-negative Matrix Factorization (PNMF). FNMF adds both the non-negative constraint and the Fisher constraint to matrix factorization. The Fisher constraint maximizes the between-class scatter and minimizes the within-class scatter of face samples. Subsequently, FNMF improves the capability of face recognition. PNMF adds the non-negative constraint and characteristics of PCA, such as maximizing the variance of output coordinates, orthogonal bases, etc. to matrix factorization. Therefore, we can get intuitive features and desirable PCA characteristics. Our experiments show that FNMF and PNMF achieve better face recognition performance than NMF and Local NMF.
8145594
Effects of global interconnect optimizations on performance estimation of deep submicron design
In this paper, we quantify the impact of global interconnect optimization techniques that address such design objectives as delay, peak noise, delay uncertainty due to noise, power, and cost. In doing so, we develop a new system-performance simulation model as a set of studies within the MARCO GSRC Technology Extrapolation (GTX) system. We model a typical point-to-point global interconnect and focus on accurate assessment of both circuit and design technology with respect to such issues as inductance, signal line shielding, dynamic delay, buffer placement uncertainty and repeater staggering. We demonstrate, for example, that optimal wire sizing models need to consider inductive effects-and that use of more accurate (-1,3) worst-case capacitive coupling noise switch factors substantially increases peak noise estimates compared to traditional (0,2) bounds. We also find that optimal repeater sizes are significantly smaller than conventional models would suggest, especially when considering energy-delay issues.
146722
Adaptive dissemination of dynamic information services in an extended data broadcast environment
Considering that most information broadcast services generate data dynamically in practice, we devise in this paper an extended data broadcast mechanism which supports not only the original data broadcast scenario but also the dissemination of dynamic inforamtion services. Furthermore, we design an on-line loan-based slot allocation and feedback technique to tolerate the dynamic changes of broadcast traffic and accordingly perform the adaptation of service classification, bandwidth allocation and broadcast schedule so as to enhance the performance. Our experimental studies show that the extended data broadcast mechanism associated with the loan-based feedback technique is able to achieve a substantial saving in message traffic for dynamic information dissemination in wireless networks.