13620764
Multi-level differential surface representation based on local transformations
One crucial issue of multi-resolution surface representations is how to effectively record and reconstruct geometric details among surface levels. Standard multi-resolution techniques encode details directly as local displacements in the vertices, and may produce unplausible results when the base level endures large deformations. In this paper we propose an alternative detail representation and reconstruction scheme, based on local transformations on a per-triangle basis. While more storage is required, recording details as local transformations favors global coupling of geometric details and allows for large-scale surface manipulations. By modeling the scale components of the surface modifications as a set of deforming factors, we achieved detail-preserving reconstruction results naturally under very large deformations. Comprehensive experimental results verify the efficiency and robustness of our approach.
28175373
Mesh editing with poisson-based gradient field manipulation
In this paper, we introduce a novel approach to mesh editing with the Poisson equation as the theoretical foundation. The most distinctive feature of this approach is that it modifies the original mesh geometry implicitly through gradient field manipulation. Our approach can produce desirable and pleasing results for both global and local editing operations, such as deformation, object merging, and smoothing. With the help from a few novel interactive tools, these operations can be performed conveniently with a small amount of user interaction. Our technique has three key components, a basic mesh solver based on the Poisson equation, a gradient field manipulation scheme using local transforms, and a generalized boundary condition representation based on local frames. Experimental results indicate that our framework can outperform previous related mesh editing techniques.
17411566
Enabling large-scale multicast simulation by reducing memory requirements
The simulation of large-scale multicast networks often requires a significant amount of memory that can easily exceed the capacity of current computers, both because of the inherently large amount of state necessary to simulate message routing and because of design oversights in the multicast portion of existing simulators. We describe three approaches to substantially reduce the memory required by multicast simulations: 1) We introduce a novel technique called "negative forwarding table" to compress multicast routing state. 2) We aggregate the routing state objects from one replicator per router per group per source to one replicator per router. 3) We employ the NIx-Vector technique to replace the original unicast IP routing table. We implemented these techniques in the ns2 simulator to demonstrate their effectiveness. Our experiments show that these techniques enable packet level multicast simulations on a scale that was previously unachievable on modern workstations using ns2.
7047265
Privacy-preserving alert correlation: a concept hierarchy based approach
With the increasing security threats from infrastructure attacks such as worms and distributed denial of service attacks, it is clear that the cooperation among different organizations is necessary to defend against these attacks. However, organizations' privacy concerns for the incident and security alert data require that sensitive data be sanitized before they are shared with other organizations. Such sanitization process usually has negative impacts on intrusion analysis (such as alert correlation). To balance the privacy requirements and the need for intrusion analysis, we propose a privacy-preserving alert correlation approach based on concept hierarchies. Our approach consists of two phases. The first phase is entropy guided alert sanitization, where sensitive alert attributes are generalized to high-level concepts to introduce uncertainty into the dataset with partial semantics. To balance the privacy and the usability of alert data, we propose to guide the alert sanitization process with the entropy or differential entropy of sanitized attributes. The second phase is sanitized alert correlation. We focus on defining similarity functions between sanitized attributes and building attack scenarios from sanitized alerts. Our preliminary experimental results demonstrate the effectiveness of the proposed techniques
12991047
The Application of Adaptive Partitioned Random Search in Feature Selection Problem
Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.
11019838
Intelligent Decision Support for Family Financial Planning
In today’s financial markets characterized by constantly changing tax laws and increasingly complex transactions, the demand for family financial planning (FFP) services is rising dramatically. However, the current trend to develop advisory systems that focus mainly on the financial or investment side fails to consider the whole picture of FFP. Separating financial or investment advice from legal and accounting advice may result in conflicting advice or important omissions that could lead to users suffering financial loss. In this paper, we propose a conceptual model for FFP decision-making process, followed by a novel architecture to support an aggregated FFP decision process by utilizing intelligent-agents and Web-services technology. A prototype system for supporting FFP decision is presented to demonstrate the advances of the proposed Web-service multi-agents-based system architecture and business value.
424382
An Efficient Computational Method for Globally Optimal Threading
Computational recognition of native-like folds of an anonymous amino acid sequence from a protein fold database is considered to be a promising approach to the three-dimensional (3D) fold prediction of the amino acid sequence. We present a new method for protein fold recognition through optimally aligning an amino acid sequence and a protein fold template (protein threading). The fitness of aligning an amino acid sequence with a fold template is measured by (1) the singleton fitness, representing the compatibility of substituting one amino acid by another and the combined preference of secondary structure and solvent accessibility for a particular amino acid, (2) the pairwise interaction, representing the contact preference between a pair of amino acids, and (3) alignment gap penalties. Though a protein threading problem so defined is known to be NP-hard in the most general sense, our algorithm runs efficiently if we place a cutoff distance on the pairwise interactions, as many of the existing threading programs do. For an amino acid sequence of size n and a fold template of size m with M core secondary structures, the algorithm finds an optimal alignment in O (Mn1.5C + 1 + mnC + 1) time and O (MnC + 1) space, where C is a (small) nonnegative integer, determined by a particular mathematical property of the pairwise interactions. As a case study, we have demonstrated that C is less than or equal to 4 for about 75% of the 293 unique folds in our protein database, when pairwise interactions are restricted to amino acids < or = 7 A apart (measured between their beta carbon atoms). An approximation scheme is developed for fold templates with C > 4, when threading requires too much memory and time to be practical on a typical workstation.