7085694
A Class-Based Search System in Unstructured P2P Networks
Efficient searching is one of the important design issues in peer-to-peer (P2P) networks. Among various searching techniques, semantic-based searching has drawn significant attention recently. Gnutella-like efficient searching system (GES) in the work of Zhu et al. (2005) is such a system. GES derives a node vector, a semantic summary of all of the documents on a node, based on vector space model (VSM). The topology adaptation algorithm and search protocol are then designed according to the similarity between node vectors of different nodes. However, although GES is suitable when the distribution of documents in each node is uniform, it may not be efficient when the distribution is diverse. When there are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES and present a class-based semantic searching system (CSS). It makes use of a data clustering algorithm, online spherical k-means clustering (OSKM) in the work of Zhang (2005), to cluster all documents on a node into several classes. Each class can be viewed as a virtual node. Virtual nodes are connected through virtual links. As a result, class vector replaces node vector and plays an important role in the class-based topology adaptation and search process, which makes CSS very efficient. Our simulation using the IR benchmark TREC collection demonstrates that CSS outperforms GES in terms of higher recall, higher precision and lower search cost.
16257632
XYZ: a scalable, partially centralized lookup service for large-scale peer-to-peer systems
Peer-to-peer (P2P) systems are characterized by direct access between peer computers, rather than through a centralized server. File sharing is the dominant P2P application on the Internet, allowing users to easily contribute, search and obtain content. P2P systems can be categorized by the degrees of centralization. For fully centralized systems, the lookup service will not be available when the central directory server is down. For purely decentralized and unstructured systems, since there is no information about which nodes are likely to have the relevant files, searching essentially amounts to random search. This makes the lookup service unscalable and unpredictable. The objective of this paper is to design a partially centralized, scalable and self-organizing lookup service (XYZ) for wide area P2P systems. A clustering method is used to create the system backbone by connecting the cluster heads together and a color clustering method is adopted to create color overlays and minimize the searching space. Simulations and analysis are also provided. Extensions are proposed to achieve better performance
17371165
QoS Routing in Hypercube Multicomputers
In this paper, we present a coding method to capture QoS information in hypercube multicomputers. The coding method is based on a localized algorithm where each node interacts with its neighbors to gather QoS information. Specifically, each node maintains a QoS vector where the k-th element represents the guaranteed QoS performance to a destination that is k hops away. The localized algorithm exhibits desirable properties of self-stabilizing, self-optimizing, and self-healing. Simulation results show that this coding method provides a good approximation of the minimum QoS value to a k-hop destination and, at the same time, uses a relatively small number of packets to propagate a change in link state (QoS value) compared with the classical distributed Bellman-Ford method.
2254959
Local-Safety-Information-Based Broadcasting in Hypercube Multicomputers with Node and Link Faults
This paper presents a method to cope with fault-tolerant broadcasting in hypercube multicomputers with both node and link faults. The local safety concept is extended to faulty hypercubes with both node and link faults. The local-safety-based algorithm is used in a fully unsafe hypercube, where there is no safe node. A fully unsafe hypercube can be split into a set of maximal safe subcubes. We show that if these maximal safe subcubes meet certain requirements given in the paper, broadcasting can still be carried out successfully and in some case optimal broadcast is still possible. The method is extended to fault-tolerant routing and multicasting when the system contains both node and link faults.
27746153
Broadcasting in Faulty Cube-Connected-Cycles with Minimum Recovery Time
We propose a broadcasting algorithm for a faulty Cube-Connected-Cycles multiprocessor. The broadcasting algorithm delivers a message to all the nodes non-redundantly using a binomial tree. The traffic generated because of the recovery is guaranteed to be minimum.
7582647
On self-similarity and hamiltonicity of dual-cubes
The dual-cube is a newly proposed topology for interconnection networks, which uses low dimensional hypercubes as building blocks. The primary advantages of the dual-cube over the hypercube are that, with the same node degree n, the dual-cube contains 2/sup n-1/ times more nodes than the hypercube and, with the same amount of nodes, the dual-cube has approximately 50% less links than the hypercube. This paper was focused on the investigations of the structural self-similarity and the Hamiltonian property of the dual-cube. It was shown that a dual-cube can be recursively constructed from lower dimensional dual-cubes and, conversely, a dual-cube can be recursively decomposed into lower dimensional dual-cubes. It was also proved that all dual-cubes are Hamiltonian. There exist multiple Hamiltonian cycles on a dual-cube, among which, [(n-1)/2] cycles are edge-disjoint. It was illustrated that rings and linear arrays can be effectively emulated on dual-cubes. Some strategies for track sharing in efficient VLSI layout design were also discussed.
10373687
The effect of dissipation on solutions of the complex KdV equation
It is known that some periodic solutions of the complex KdV equation with smooth initial data blow up in finite time. In this paper, we investigate the effect of dissipation on the regularity of solutions of the complex KdV equation. It is shown here that if the initial datum is comparable to the dissipation coefficient in the L2-norm, then the corresponding solution does not develop any finite-time singularity. The solution actually decays exponentially in time and becomes real analytic as time elapses. Numerical simulations are also performed to provide detailed information on the behavior of solutions in different parameter ranges.
412675
Linear Asymmetric Classifier for cascade detectors
The detection of faces in images is fundamentally a rare event detection problem. Cascade classifiers provide an efficient computational solution, by leveraging the asymmetry in the distribution of faces vs. non-faces. Training a cascade classifier in turn requires a solution for the following subproblems: Design a classifier for each node in the cascade with very high detection rate but only moderate false positive rate. While there are a few strategies in the literature for indirectly addressing this asymmetric node learning goal, none of them are based on a satisfactory theoretical framework. We present a mathematical characterization of the node-learning problem and describe an effective closed form approximation to the optimal solution, which we call the Linear Asymmetric Classifier (LAC). We first use AdaBoost or AsymBoost to select features, and use LAC to learn a linear discriminant function to achieve the node learning goal. Experimental results on face detection show that LAC can improve the detection performance in comparison to standard methods. We also show that Fisher Discriminant Analysis on the features selected by AdaBoost yields better performance than AdaBoost itself.
15943479
Efficient face candidates selector for face detection
Abstract In this paper an efficient face candidates selector is proposed for face detection tasks in still gray level images. The proposed method acts as a selective attentional mechanism. Eye-analogue segments at a given scale are discovered by finding regions which are roughly as large as real eyes and are darker than their neighborhoods. Then a pair of eye-analogue segments are hypothesized to be eyes in a face and combined into a face candidate if their placement is consistent with the anthropological characteristic of human eyes. The proposed method is robust in that it can deal with illumination changes and moderate rotations. A subset of the FERET data set and the BioID face database are used to evaluate the proposed method. The proposed face candidates selector is successful in 98.75% and 98.6% cases, respectively.
13581814
All-optical network demonstrator program centered around a testbed for verifying and validating next generation optical networks
The all-optical network (AON) demonstrator program is centered around a long-term trial testbed for the validation and verification of key network building blocks, scalable architectures, control and management solutions for next generation wavelength division multiplexing networks. The AON testbed has already validated certain system level concepts in both physical (performance characterization) and upper layers (network management). Performance characterization of optical signals was used to develop real-time monitoring techniques of the key network parameters for detection, identification and localization of signal degradations. Upper layer proof of concepts verified network level management approaches and optimizations for large and challenging applications. A prototype of a network management system was developed for customer-managed networks.
2718321
Face recognition with one training image per person
At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. projection-combined principal component analysis, (PC)2A, is proposed. (PC)2A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)2A achieves 3-5% higher accuracy than the standard eigenface technique through using 10-15% fewer eigenfaces.