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"abstract": "The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user- and context-dependent visual exploration systems.",
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"abstract": "The query time was shortened efficiently by adopting sampling method for storing historical stream data. However, important information was discarded in a large possibility because all data were treated non-discriminately with same sampling probability. In this paper, we addressed the problem by introducing a method for storing historical stream data based on degree of interest. The method used parameter Q to indicate the importance of each data, based on which a sampling algorithm had better effect on maintaining the important information. The experimental results showed that the degree of interest based method had fine performance in the query accuracy, especially for simple query on the historical stream data.",
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"fullName": "Zhi-Kai Zhao",
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"fullName": "Jian Cheng",
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"fullName": "Nan-Nan Lu",
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"abstract": "Citation analysis has been applied in various contexts for different purposes such as impact factors estimation, co-citation pattern analysis, community partitioning, and knowledge visualization etc. We employed graph theory and authoritative sources identification techniques, augmented with visualization tools to discover critical research focuses from the citation graph. The citation graph was built from data retrieved from the CiteSeer database via a querying robot. Two experiments were carried out to identify important research focuses from the citation graph with promising results. Established research focuses as well as new research focuses were successfully identified by the method we proposed and tried. Researchers new to a field may use this method to locate important literature in various fields, which in turn will facilitate their learning and studying.",
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"affiliation": "National Taipei University",
"fullName": "Tsung Teng Chen",
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"affiliation": "National Taipei University",
"fullName": "Liang Qi Xie",
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"title": "Acquisition of state transitions in neural network",
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"abstract": "In the artificial intelligence, the breadth-first search is optimal with uniform cost. But it takes long time to obtain the solution. Neural networks process states transitions in parallel with learning ability. We developed a search procedure of states transition doing the the breadth-first, in the neural network. First, the input pattern states are self-organized in the neural network, which consists of the Kohonen layer followed by the state planning layer. The state planning layer makes lateral connections between cells of transitions. Then, the initial and the target states are given as a problem. The network shows an optimal state transition pathway in the neuron firings. Next, the state transition procedure is developed for the formation of the concept of action planning. Here, as the action planning, an integration between the symbols and the action pattern is carried out in the extended neural network.",
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"content": "In the artificial intelligence, the breadth-first search is optimal with uniform cost. But it takes long time to obtain the solution. Neural networks process states transitions in parallel with learning ability. We developed a search procedure of states transition doing the the breadth-first, in the neural network. First, the input pattern states are self-organized in the neural network, which consists of the Kohonen layer followed by the state planning layer. The state planning layer makes lateral connections between cells of transitions. Then, the initial and the target states are given as a problem. The network shows an optimal state transition pathway in the neuron firings. Next, the state transition procedure is developed for the formation of the concept of action planning. Here, as the action planning, an integration between the symbols and the action pattern is carried out in the extended neural network.",
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"affiliation": "Dept. of Intelligence & Comput. Sci., Nagoya Inst. of Technol., Japan",
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"abstract": "In this paper, we extend the generalized fair reachability notion to cyclic protocols with nondeterministic and internal transitions. By properly incorporating internal transitions into the formulation of fair progress vectors, we prove that with only a few modifications, all the results established for cyclic protocols without nondeterministic and internal transitions still hold even if nondeterministic and internal transitions are allowed. We identify indefiniteness as a new type of logical error resulting from reachable internal execution cycles and show that it can also be detected for the class of cyclic protocols with finite fair reachable state spaces with finite extension",
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"abstract": "In this paper we present a new indexing-based approach to fingerprint matching technique. Our proposed approach is the idea of associating a unique topological structure with the fingerprint minutiae using Voronoi diagram (VD). We utilize well-known algorithms and techniques from the initial step of image registration up to feature extraction (Minutiae extraction). Several image processing techniques such as orientation, segmentation, Gabor Filter, binarization, and thinning are employed in order to have a valid feature extraction. Using VD on the topological structure of minutiae set, results in a VD with minutiae as vertices. In the VD diagram we find a central cell; and such a region is unique based on our approach. The central cell (CC) is used for local matching. This approach rejects non-similar fingerprints instantly and has good accuracy and performance in comparison with existing algorithms. In addition, we also propose an alternative algorithm that is customized in order to take into account, distortion (non-linear transformation) based on our primary algorithm. The experiment is performed on FVC 2006 databases, and the result indicates the validity of our algorithm.",
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"abstract": "Vague sets, characterized by a truth-membership function and a false-membership function, was introduced by Gau and Buehrer [1]. In this paper, we define the concepts of vague preference relation and incomplete vague preference relation. Approaches to group decision making based on vague preference relations and based on incomplete vague preference relations respectively are proposed. Then, the vague arithmetic averaging operator and vague weighted arithmetic averaging operator are used to aggregate vague preference information. The ranking method proposed is applied to ranking and selection of alternatives. Finally, by a numerical example, we illustrate the proposed approach.",
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"title": "Visualizing edge-edge relations in graphs",
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"abstract": "Graphs are used to model relations between sets of objects. Objects are represented by vertices and relations by edges of the graph. Besides vertex-vertex relations, in some application domains also relations between edges exist. Our new visualization approach supports the investigation of both relation types in one diagram. Edge-edge relations are visualized as curves that are directly integrated into the node-link diagram that represents the object-relation structure. In contrast, vertex-vertex relations are illustrated distinguishably from edge-edge relations using straight links as representations. While the shape of links is used to differentiate between the relation types, the weights of the edge-edge relations are mapped to the width and color of the curves. To facilitate an extensive analysis of interrelations, our approach incorporates several interaction techniques that can be used for filtering and highlighting. The usability of our visualization is demonstrated with two case studies in the application domains of bioinformatics and financial services.",
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"abstract": "Current studies on consumers’ perceived trust of virtual community concentrate on the antecedents of the community technology factors, but relative few about social factors. From the view of social factors, this paper develops the conceptual model of consumers’ perceived trust of the transaction virtual community in which community’ s transparency and community’ s information comprehensiveness work as antecedent variables, and consumers’ loyalty to the cummunity and purchase intention work as consequent variables.",
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"abstract": "Asynchronous circuit design shows many similarities with software design. This is due to the modular construction style associated with asynchronous circuits, where each asynchronous module can be designed as a standalone object communicating with other modules. We propose a visualisation system for asynchronous circuit design tools, based on clustered graph visualisation and coordinated views. The novelty of our approach is to apply classical software visualisation techniques to a hardware environment and take advantage of properties specific to our asynchronous design flow. This system is based on a control and data flow graph representation of the asynchronous circuit compiled from a high-level description by a syntax-directed transparent process and transferable to the final hardware circuit by a direct synthesis process. Compared to software compilation flows, this transparent design flow offers opportunities for visualisation, with representations at different stages being easily mapped onto each other to combine their visualisation qualities. Each representation also exhibits properties based on some physical attributes of the final hardware circuit, allowing us to render some abstract properties on top of real-world-based ones. This paper shows how the handshake circuit graph is used as the underlying structure onto which properties extracted from other structures are mapped, and then how this structured graph is used as the central piece in a coordinated views environment. These visualisation techniques have been exercised by designers optimising the design of a fully asynchronous ARM processor.",
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"abstract": "Radial, space-filling visualizations can be useful for depicting information hierarchies, but they suffer from one major problem. As the hierarchy grows in size, many items become small, peripheral slices that are difficult to distinguish. We have developed three visualization/interaction techniques that provide flexible browsing of the display. The techniques allow viewers to examine the small items in detail while providing context within the entire information hierarchy. Additionally, smooth transitions between views help users maintain orientation within the complete information space.",
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"abstract": "This paper proposes a novelty neural network model by using generalized kernel functions for the hidden layer of a feed forward network (Generalized Radial Basis Functions, GRBF), where the architecture, weights and node typology are learned through an evolutionary programming algorithm. This new kind of model is compared with the corresponding models with standard hidden nodes: Product Unit Neural Networks (PUNN), Multilayer Perceptrons (MLP) and the RBF neural networks. The methodology proposed is tested using six benchmark classification datasets from well-known machine learning problems. Generalized basis functions are found to present a better performance than the other standard basis functions for the task of classification.",
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"abstract": "Groundwater is the source of main agricultural, industrial and domestic water in the Alluvial Plain of Hutu River, the pollution degree of groundwater directly affectshabitants¡¯ state of health in the area. In order to explain the condition of shallow groundwater organic pollution under the influence of human activities, the Alluvial Plain of Hutuo River was selected as the main study area, and then by a large amount of groundwater sampling, analysis and test, we analyzed the characteristics of shallow groundwater organic pollution. The results show that shallow groundwater has been under threat of organic pollution in the area, the main detected organic components are halogenated hydrocarbons, polyaromatics and chlorobenzenes, among which the detection rate of trichloromethane amounts to 14.02%; Over-standard organics samples are sparse, but the over-standard concentrations are high, the main over-standard component istetrachloro methane. Because of the high toxicity of organics, organic pollution has become unable to be neglected in the study area.",
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"content": "Groundwater is the source of main agricultural, industrial and domestic water in the Alluvial Plain of Hutu River, the pollution degree of groundwater directly affectshabitants¡¯ state of health in the area. In order to explain the condition of shallow groundwater organic pollution under the influence of human activities, the Alluvial Plain of Hutuo River was selected as the main study area, and then by a large amount of groundwater sampling, analysis and test, we analyzed the characteristics of shallow groundwater organic pollution. The results show that shallow groundwater has been under threat of organic pollution in the area, the main detected organic components are halogenated hydrocarbons, polyaromatics and chlorobenzenes, among which the detection rate of trichloromethane amounts to 14.02%; Over-standard organics samples are sparse, but the over-standard concentrations are high, the main over-standard component istetrachloro methane. Because of the high toxicity of organics, organic pollution has become unable to be neglected in the study area.",
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"abstract": "Organic Computing Systems feature self-organizing techniques to manage the rising complexity of distributed systems. This paper introduces an implementation of self-configuration, self-healing and self-optimization by means of an Organic Manager. The Organic Manager is based on an automated planner and integrated in a middleware. The Organic Manager unites formerly distributed self-x features to use synergetic effects. An evaluation with four scenarios compares the behavior of the system under two different planning models. We show that an automated planner can lead to an optimized system quickly and reliably.",
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"abstract": "Graphic Processing Unit (GPU) has involved into a parallel computation for it’s massively multi threaded architecture. Due to its high computational power, GPU has been used to deal with many problems that can be easily parallelized. This paper will present a GPU based spot noise parallel algorithm for 2D vector field visualization. It uses spot noise method with GPU resources and compute unified device architecture (CUDA) to visualize 2D vector field. Vector field are partitioned to multiple thread so that a large number of data are processed simultaneously. Fast on-chip shared memory is used on GPU to optimize the performance and a data transformation mechanism between host and device is presented. The parallel algorithm applies these strategies to a 2D velocity field and obtains up to 16X speedup compared with conventional sequential computation. It is suitable for interactive applications and in-time remote visualization of vector fields.",
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"content": "Graphic Processing Unit (GPU) has involved into a parallel computation for it’s massively multi threaded architecture. Due to its high computational power, GPU has been used to deal with many problems that can be easily parallelized. This paper will present a GPU based spot noise parallel algorithm for 2D vector field visualization. It uses spot noise method with GPU resources and compute unified device architecture (CUDA) to visualize 2D vector field. Vector field are partitioned to multiple thread so that a large number of data are processed simultaneously. Fast on-chip shared memory is used on GPU to optimize the performance and a data transformation mechanism between host and device is presented. The parallel algorithm applies these strategies to a 2D velocity field and obtains up to 16X speedup compared with conventional sequential computation. It is suitable for interactive applications and in-time remote visualization of vector fields.",
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