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"title": "Information Technology and Computer Science, International Conference on",
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"doi": "10.1109/ITCS.2009.179",
"title": "Numerical Computation of Laminar Flow Pipeline Transport Axial Flow Field",
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"abstract": "The finite difference method is adopted to compute the axial velocity of coal water slurry (CWS) pipeline transport axial flow field flowing in the laminar flow, which could be described by Poisson partial differential equation. In this work, the finite difference method was used to compute the computational axial velocity. The axial velocity contour of CWS pipeline axial flow field was presented in the three-dimensional (3D) space, and the relationship curve between axial velocity and cross sectional diameter of CWS pipeline axial flow field was shown in a two-dimensional (2D) plane. The presentation results showed that the axial velocity of the CWS pipeline axial flow field increased from the boundary of the pipeline wall to the center of the CWS pipeline axial flow field, the increasing tendency between axial velocity and cross sectional diameter of the CWS pipeline axial flow field conformed to nonlinear relationship, furthermore, and the relationship curves between axial velocity and cross sectional diameter approximated the parabolic contour. Furthermore, the conclusions of the numerical computation of laminar flow pipeline transport axial flow field studied in the finite element method and finite difference method was compared.",
"abstracts": [
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"content": "The finite difference method is adopted to compute the axial velocity of coal water slurry (CWS) pipeline transport axial flow field flowing in the laminar flow, which could be described by Poisson partial differential equation. In this work, the finite difference method was used to compute the computational axial velocity. The axial velocity contour of CWS pipeline axial flow field was presented in the three-dimensional (3D) space, and the relationship curve between axial velocity and cross sectional diameter of CWS pipeline axial flow field was shown in a two-dimensional (2D) plane. The presentation results showed that the axial velocity of the CWS pipeline axial flow field increased from the boundary of the pipeline wall to the center of the CWS pipeline axial flow field, the increasing tendency between axial velocity and cross sectional diameter of the CWS pipeline axial flow field conformed to nonlinear relationship, furthermore, and the relationship curves between axial velocity and cross sectional diameter approximated the parabolic contour. Furthermore, the conclusions of the numerical computation of laminar flow pipeline transport axial flow field studied in the finite element method and finite difference method was compared.",
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"normalizedAbstract": "The finite difference method is adopted to compute the axial velocity of coal water slurry (CWS) pipeline transport axial flow field flowing in the laminar flow, which could be described by Poisson partial differential equation. In this work, the finite difference method was used to compute the computational axial velocity. The axial velocity contour of CWS pipeline axial flow field was presented in the three-dimensional (3D) space, and the relationship curve between axial velocity and cross sectional diameter of CWS pipeline axial flow field was shown in a two-dimensional (2D) plane. The presentation results showed that the axial velocity of the CWS pipeline axial flow field increased from the boundary of the pipeline wall to the center of the CWS pipeline axial flow field, the increasing tendency between axial velocity and cross sectional diameter of the CWS pipeline axial flow field conformed to nonlinear relationship, furthermore, and the relationship curves between axial velocity and cross sectional diameter approximated the parabolic contour. Furthermore, the conclusions of the numerical computation of laminar flow pipeline transport axial flow field studied in the finite element method and finite difference method was compared.",
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"fullName": "Yuming Xia",
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"fullName": "Weidong Wang",
"givenName": "Weidong",
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"abstract": "We introduce a non-uniform composition that integrates multiple rendering styles in a picture driven by an importance map. This map, either issued from saliency estimation or designed by a user, is introduced both in the creation of the multiple styles and in the final composition. Our approach accommodates a variety of stylization techniques, such as color desaturation, line drawing, blurring, edge-preserving smoothing and enhancement. We illustrate the versatility of the proposed approach and the variety of rendering styles on different applications such as images, videos, 3D scenes and even mixed reality. We also demonstrate that such an approach may help in directing user attention.",
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"abstract": "In this paper we evaluate the effect of noise on community scoring and centrality-based parameters with respect to two different aspects of network analysis: (i) sensitivity, that is how the parameter value changes as edges are removed and (ii) reliability in the context of message spreading, that is how the time taken to broadcast a message changes as edges are removed. Our experiments on synthetic and real-world networks and three different noise models demonstrate that for both the aspects over all networks and all noise models, permanence qualifies as the most effective metric. For the sensitivity experiments closeness centrality is a close second. For the message spreading experiments, closeness and betweenness centrality based initiator selection closely competes with permanence. This is because permanence has a dual characteristic where the cumulative permanence over all vertices is sensitive to noise but the ids of the top-rank vertices, which are used to find seeds during message spreading remain relatively stable under noise.",
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"content": "In this paper we evaluate the effect of noise on community scoring and centrality-based parameters with respect to two different aspects of network analysis: (i) sensitivity, that is how the parameter value changes as edges are removed and (ii) reliability in the context of message spreading, that is how the time taken to broadcast a message changes as edges are removed. Our experiments on synthetic and real-world networks and three different noise models demonstrate that for both the aspects over all networks and all noise models, permanence qualifies as the most effective metric. For the sensitivity experiments closeness centrality is a close second. For the message spreading experiments, closeness and betweenness centrality based initiator selection closely competes with permanence. This is because permanence has a dual characteristic where the cumulative permanence over all vertices is sensitive to noise but the ids of the top-rank vertices, which are used to find seeds during message spreading remain relatively stable under noise.",
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"title": "Inferring Community Members in Social Networks by Closeness Centrality Examination",
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"abstract": "It is important task to discover communities or hidden groups by analyzing the messages collected in social networks. For the case when some members of a community are known, a proper method is still necessary to infer the remaining community members. To address such an issue, we develop a closeness centrality examination algorithm to obtain the remaining community members with some known community members. In the proposed model, the message connections among all social network members is captured by a weighted graph model where the edges are assigned with weights derived from the sensitivity of topics contained in the messages by text analysis. In addition, the nodes of known community members form a central sub tree in the weighted graph model. The suspicious priority list of possible community members is obtained by calculating a closeness centrality score to the central sub tree. With the priority list, the remaining community members can be determined using cluster analysis and outlier analysis. The proposed method is validated with experiments.",
"abstracts": [
{
"abstractType": "Regular",
"content": "It is important task to discover communities or hidden groups by analyzing the messages collected in social networks. For the case when some members of a community are known, a proper method is still necessary to infer the remaining community members. To address such an issue, we develop a closeness centrality examination algorithm to obtain the remaining community members with some known community members. In the proposed model, the message connections among all social network members is captured by a weighted graph model where the edges are assigned with weights derived from the sensitivity of topics contained in the messages by text analysis. In addition, the nodes of known community members form a central sub tree in the weighted graph model. The suspicious priority list of possible community members is obtained by calculating a closeness centrality score to the central sub tree. With the priority list, the remaining community members can be determined using cluster analysis and outlier analysis. The proposed method is validated with experiments.",
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"affiliation": null,
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"proceeding": {
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"title": "Personalizing EigenTrust in the Face of Communities and Centrality Attack",
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"abstract": "EigenTrust (ET) is a renowned algorithm for reputation management in adversarial P2P systems. It incorporates the opinions of all peers in the network to compute a global trust score for each peer based on its past behavior, and relies on a set of pre-trusted nodes to guarantee that malicious nodes cannot subvert the system. In this paper, we show that ET is vulnerable to community structure and a novel targeted attack based on eigenvector centrality, since ET ranks nodes close to the pre-trusted ones higher than those further away. To address these shortcomings, we propose Personalized EigenTrust (PET) which (i) enables each user to choose her trusted peers from the social network of peers, thereby eliminating the need of pre-trusted nodes and making the system autonomous, (ii) is effective in networks operating under various transaction models based on distributions such as random, community-like and power-law, and (iii) is robust to many types of attacks including the targeted one based on eigenvector centrality. Our simulation results reveal that PET outperforms ET under diverse transaction models and attack strategies.",
"abstracts": [
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"abstractType": "Regular",
"content": "EigenTrust (ET) is a renowned algorithm for reputation management in adversarial P2P systems. It incorporates the opinions of all peers in the network to compute a global trust score for each peer based on its past behavior, and relies on a set of pre-trusted nodes to guarantee that malicious nodes cannot subvert the system. In this paper, we show that ET is vulnerable to community structure and a novel targeted attack based on eigenvector centrality, since ET ranks nodes close to the pre-trusted ones higher than those further away. To address these shortcomings, we propose Personalized EigenTrust (PET) which (i) enables each user to choose her trusted peers from the social network of peers, thereby eliminating the need of pre-trusted nodes and making the system autonomous, (ii) is effective in networks operating under various transaction models based on distributions such as random, community-like and power-law, and (iii) is robust to many types of attacks including the targeted one based on eigenvector centrality. Our simulation results reveal that PET outperforms ET under diverse transaction models and attack strategies.",
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"fno": "4651a503",
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"title": "Topology of the Documentation Landscape",
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"abstract": "Every software system (ideally) comes with one or more forms of documentation. Beside source code comments, other structured and unstructured sources (e.g., design documents, API references, wikis, usage examples, tutorials) constitute critical assets. Cloud-based repositories for collaborative development (e.g., GitHub, Bitbucket, GitLab) provide many functionalities to create, persist, and version documentation artifacts. On the other hand, the last decade has seen the rise of rich instant messaging clients used as global software community platforms (e.g., Slack, Discord). Although completely detached from a specific versioning system or development workflow, they allow developers to discuss implementation issues, report bugs, and, in general, interact with one another.We refer to this evolving heterogeneous collection of information sources and documentation artifacts as the documentation landscape. It is important to have tools to extract information from these sources and integrate them in a topological visualization, to ease comprehension of a software system. How can we automatically generate this topology? How can we link elements in the topology back to the source code they refer to?The goal of this PhD research is to automatically mine the documentation landscape of a system by disclosing pieces of information to aid, for example, in program maintenance tasks. We present our classification of possible documentation sources. The long term vision is to provide a domain model of the documentation landscape to build, visualize, and explore its instances for real software systems and evaluate the usefulness of the metaphor we propose.",
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"abstract": "Clustering is a discovery process in data mining an can be used to group together the objects of a database into meaningful subclasses which serve as the foundation for other data analysis techniques. In this paper, we focus on dealing with a set of spatial data. For the spatial data, the clustering problem becomes that of finding the densely populate regions of the space and thus grouping these regions into clusters such that the intracluster similarity is maximized and the intercluster similarity is minimized. We develop a novel hierarchical clustering algorithm that uses a hypergraph to represent a set of spatial data. This hypergraph is initially constructed from the Delaunay triangulation graph of the data set and can correctly capture the relationships among sets of data points. Two phases are developed for the proposed clustering algorithm to find the clusters in the data set. We evaluate our hierarchical clustering algorithm with some spatial data sets in which contain clusters of different sizes, shapes, densities, and noise. Experimental results on these data sets are very encouraging.",
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"abstract": "The fluid-flow approximation models have been during last few years to analyze the dynamics and stability of TCP/RED connections in wired networks. The model assumes that the fluctuations of parameters of the connections are relatively low. The fluid flow models base on the linearization and use of control theory analysis tools to verify the stability and behavior of the analyzed system. In this article we use some extensions of fluid flow approximation to support time division multiplexing used in WiMAX wireless networks. We describe the modification of fluid flow equations to incorporate the delivery of packets as an on-off source with on and off times modulated by the WiMAX scheduling algorithm. The results of the fluid flow model have been compared to the measurement in a small test bed. We also show how to analyze the stability of the connection using control theory approach.",
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"abstract": "Magnetic fluid is a liquid magnetic material which could reflect the changes of magnetic field rapidly. For application research, the characteristics of magnetic fluid such as supermagnetism, lubricity and fluidity were introduced and analyzed. A gas flow sensor was designed and researched utilizing the magnetic fluid as a sensing medium. The combination of the magnetic fluid and magnet was designed as the magnetic core in the flow sensor. The structure of the flow sensor was designed and analyzed. The measuring work principle of the gas flow sensor with magnetic fluid was studied. By calculating and analyzing, the flow for measuring is related to the parameters including the elasticity coefficient of the spring, the motion distance of the magnetic core, the moving time of the magnetic core, the cross section area of the magnetic core, and the cross section of the measuring pipe for the gas passing through. The gas flow sensor with magnetic fluid is efficient and feasible.",
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"content": "Magnetic fluid is a liquid magnetic material which could reflect the changes of magnetic field rapidly. For application research, the characteristics of magnetic fluid such as supermagnetism, lubricity and fluidity were introduced and analyzed. A gas flow sensor was designed and researched utilizing the magnetic fluid as a sensing medium. The combination of the magnetic fluid and magnet was designed as the magnetic core in the flow sensor. The structure of the flow sensor was designed and analyzed. The measuring work principle of the gas flow sensor with magnetic fluid was studied. By calculating and analyzing, the flow for measuring is related to the parameters including the elasticity coefficient of the spring, the motion distance of the magnetic core, the moving time of the magnetic core, the cross section area of the magnetic core, and the cross section of the measuring pipe for the gas passing through. The gas flow sensor with magnetic fluid is efficient and feasible.",
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{
"affiliation": "Beijing Polytechnic,School of Automotive Engineering,Beijing,China",
"fullName": "Ruican Hao",
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"affiliation": "Beijing Polytechnic,School of Basic Courses,Beijing,China",
"fullName": "Feifei Xing",
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"affiliation": "Beijing Polytechnic,School of Automotive Engineering,Beijing,China",
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"abstract": "Retrieval of 3D models by using 2D sketch query has recently become an active area of research. However, retrieval accuracy of the modality is still much lower than using a 3D model example as query. In this paper, we propose an algorithm that employs manifold-learning based dimension reduction for sketch-based 3D model retrieval. The algorithm compares multi-view rendering of 3D models with the 2D sketch. To improve distance computation, a distance metric adapted to data sample distribution is learned by using a manifold-learning algorithm, that is, Locally Linear Embedding (LLE). In order to lower the cost of training the LLE, we reduce number of training samples by clustering, either in feature space or in view space. Experimental evaluation has shown that both view space clustering and feature space clustering lowers training cost by more than 10 times while significantly improving retrieval accuracy. A compact 50 dimensional feature after the dimension reduction is much faster to compare, and its retrieval accuracy is 40% better than the original 30k dimensional feature. In terms of training cost, view clustering approach costs 20 times less than the one using full set of features without clustering.",
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"abstract": "In this paper, 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.",
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"content": "In this paper, 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.",
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"abstract": "Object detection in clutter or occlusion is a hard problem in computer vision. We propose an object detection method based on contour grouping. Two stages are included: a novel distance transform is applied to match templates to the test image so that candidates and locations of the object are obtained; verification using shape manifold is performed to preclude outliers and identify the prior. We use the prior combined with bottom-up edge information to produce the final grouping result. Our contribution lies in two aspects: one is the novel distance transform saves much searching space; the other is introducing shape manifold in verifying candidates of grouping. Experiments show our method achieves considerable accuracy in occlusion and background clutter. Specially, the only feature used is edge and contour rather than combination of multi features.",
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"abstract": "Software designers frequently sketch when they design, particularly during the early phases of exploration of a design problem and its solution. In so doing, they shun formal design tools, the reason being that such tools impose conformity and precision prematurely. Sketching on the other hand is a highly fluid and flexible way of expressing oneself. In this paper, we present Calico, a sketch-based distributed software design tool that supports software designers with a variety of features that improve over the use of just pen-and-paper or a regular whiteboard, and are tailored specifically for software design. Calico is meant to be used on electronic whiteboards or tablets, and provides for rapid creation and manipulation of design content by sets of developers who can collaborate distributedly.",
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"content": "Reducing synchronization in iterative methods for solving large sparse linear systems may become one of the most important goals for such solvers on exascale computers. Research in asynchronous iterative methods has primarily considered basic iterative methods. In this paper, we examine how multigrid methods can be executed asynchronously. We present models of asynchronous additive multigrid methods, and use these models to study the convergence properties of these methods. We also introduce two parallel algorithms for implementing asynchronous additive multigrid, the global-res and local-res algorithms. These two algorithms differ in how the fine grid residual is computed, where local-res requires less computation than global-res but converges more slowly. We compare two types of asynchronous additive multigrid methods: the asynchronous fast adaptive composite grid method with smoothing (AFACx) and additive variants of the classical multiplicative method (Multadd). We implement asynchronous versions of Multadd and AFACx in OpenMP and generate the prolongation and coarse grid matrices using the BoomerAMG package. Our experimental results show that asynchronous multigrid can exhibit grid-size independent convergence and can be faster than classical multigrid in terms of solve wall-clock time. We also show that asynchronous smoothing is the best choice of smoother for our test cases, even when only one smoothing sweep is used.",
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"Multigrid Methods",
"Smoothing Methods",
"Synchronization",
"Iterative Methods",
"Convergence",
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"Multigrid",
"Sparse Linear Systems"
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"affiliation": "Georgia Institute of Technology",
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"affiliation": "Georgia Institute of Technology",
"fullName": "Edmond Chow",
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"abstract": "This paper introduces a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original distance metric in lower-dimensional input spaces. We restrict ourselves to pseudo-metrics that are in quadratic forms parameterized by positive semi-definite matrices. The proposed method works in both the input space and kernel in-duced feature space, and learning distance metric is formulated as a quadratic optimization problem which returns a global optimal solution. Experimental results on several databases show that the learned distance metric improves the performances of the subsequent classification and clustering algorithms.",
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"abstract": "The software clustering problem has attracted much attention recently, since it is an integral part of the process of reverse engineering large software systems. A key problem in this research is the difficulty in comparing different approaches in an objective fashion. In this paper, we present a metric that can be used in evaluating the similarity of two different decompositions of a software system.Our metric calculates a distance between two partitions of the same set of software resources. We begin by introducing the model we use. Consequently, we present a heuristic algorithm that calculates the distance in an efficient fashion. Finally, we discuss some experiments that showcase the performance of the algorithm and the effectiveness of the metric.",
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"affiliation": "University of Toronto",
"fullName": "Vassilios Tzerpos",
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{
"affiliation": "University of Waterloo",
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"title": "Distance Exponent: A New Concept for Selectivity Estimation in Metric Trees",
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"abstract": "This paper discusses the problem of selectivity estimation for range queries in metric datasets, which include vector, or dimensional, datasets as a special case. The main contribution of this paper is that, surprisingly, many different real datasets follow a \"power law\". From this observation we derive an analysis for the distance distribution of metric datasets. This is the first analysis of distance distributions for real metric datasets.We called the exponent of our power law as \"distance exponent\". We show that it plays a relevant role for the analysis of real, metric datasets. Specifically, we show (a) how to exploit the distance exponent to derive formulas for selectivity estimation of range queries and (b) how to compute it quickly from a metric index tree.We performed several experiments on many real datasets (road intersections of U.S. counties, vectors characteristics extracted from face matching systems, sets of words, distance matrixes) and synthetic datasets (Sierpinsky triangle, a 2-dimensional uniform distribution and a 2-dimensional line). Our selectivity estimation formulas are accurate, within relative error from 4% to 17%, and always within one standard deviation from the analytical results. Moreover, we present also a quick algorithm to estimate the \"distance exponent\", which gives good accuracy and saves orders of magnitude in computation time.",
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"content": "This paper discusses the problem of selectivity estimation for range queries in metric datasets, which include vector, or dimensional, datasets as a special case. The main contribution of this paper is that, surprisingly, many different real datasets follow a \"power law\". From this observation we derive an analysis for the distance distribution of metric datasets. This is the first analysis of distance distributions for real metric datasets.We called the exponent of our power law as \"distance exponent\". We show that it plays a relevant role for the analysis of real, metric datasets. Specifically, we show (a) how to exploit the distance exponent to derive formulas for selectivity estimation of range queries and (b) how to compute it quickly from a metric index tree.We performed several experiments on many real datasets (road intersections of U.S. counties, vectors characteristics extracted from face matching systems, sets of words, distance matrixes) and synthetic datasets (Sierpinsky triangle, a 2-dimensional uniform distribution and a 2-dimensional line). Our selectivity estimation formulas are accurate, within relative error from 4% to 17%, and always within one standard deviation from the analytical results. Moreover, we present also a quick algorithm to estimate the \"distance exponent\", which gives good accuracy and saves orders of magnitude in computation time.",
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