This paper addresses the community detection problem in multi-view weighted signed network. Although some methods have been developed to detect communities in multi-view network, they are mostly designed for the unwei...
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ISBN:
(纸本)9781509032068
This paper addresses the community detection problem in multi-view weighted signed network. Although some methods have been developed to detect communities in multi-view network, they are mostly designed for the unweighted unsigned case. There is still a lack of methods for multi-view weighted signed network. Since an increasing number of multi-view weighted signed networks are being generated in some applications, there is a necessity to develop a community detection approach to reveal the complicated structure of such networks. In this paper, we extend the single-view permanence model to the multi-view weighted signed case, which is a node-level model by considering four factors based on the influence of the nodes, including internal connections, total connections, maximum external connections and internal clustering coefficient. Extensive experiments are conducted on some networks to evaluate the performance of our proposed approach.
In this paper, we propose a leg-driven physiology framework for pedestrian detection. The framework is introduced to reduce the search space of candidate regions of pedestrians. Given a set of vertical line segments, ...
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ISBN:
(纸本)9781467399623
In this paper, we propose a leg-driven physiology framework for pedestrian detection. The framework is introduced to reduce the search space of candidate regions of pedestrians. Given a set of vertical line segments, we can generate a space of rectangular candidate regions, based on a model of body proportions. The proposed framework can be either integrated with or without learning-based pedestrian detection methods to validate the candidate regions. A symmetry constraint is then applied to validate each candidate region to decrease the false positive rate. The experiment demonstrates the promising results of the proposed method by comparing it with Dalal & Triggs method. For example, rectangular regions detected by the proposed method has much similar area to the ground truth than regions detected by Dalal & Triggs method.
Nonlinear clustering has attracted an increasing amount of attention recently. In this paper, we propose a new nonlinear clustering method based on Cluster Shrinking and Border Detection (CSBD). Unlike most existing c...
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ISBN:
(纸本)9781509032068
Nonlinear clustering has attracted an increasing amount of attention recently. In this paper, we propose a new nonlinear clustering method based on Cluster Shrinking and Border Detection (CSBD). Unlike most existing clustering method, the CSBD method focuses on every data point rather then the cluster centers. A novel idea, namely Cluster Shrinking, is designed to transform the original nonlinear datasets into several hyperspheres, which makes clustering work much easier. Besides, we also introduce a simple but effective Border Detection method based on histogram analysis to automatically determine the threshold parameter in Cluster Shrinking phase. Extensive experiments have been conducted to demonstrate the effectiveness of CSBD in both synthetic and real-world datasets.
Expert finding is an important technique to obtain the user authority ranking in community question answering (CQA) websites. ZhihuRank is a topic-sensitive expert finding algorithm, which is based on both LDA and Pag...
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ISBN:
(纸本)9781467390064
Expert finding is an important technique to obtain the user authority ranking in community question answering (CQA) websites. ZhihuRank is a topic-sensitive expert finding algorithm, which is based on both LDA and PageRank. Currently, with the amount of participants and documents increasing rapidly in CQA websites, how to parallel expert finding algorithms for big data analysis has received significant attention. In this paper, we find that the Spark framework is more suitable for paralleling expert finding algorithms than the MapReduce framework, which is a memory-based parallel computing model to support complicated iterative algorithms. As an example, we parallel ZhihuRank using MLlib's LDA and GraphX's PageRank in Spark. Experiments have been conducted on large-scale real data from Zhihu 1 (the most popular CQA website in China). And the experimental results confirmed the effectiveness and scalab.lity of our proposed approach.
Workflow testing is an important method of workflow analysis in design time. A challenging problem with trace-oriented test data generation in particular and trace-based workflow analysis in general is the existence o...
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ISBN:
(纸本)9789898565105
Workflow testing is an important method of workflow analysis in design time. A challenging problem with trace-oriented test data generation in particular and trace-based workflow analysis in general is the existence of infeasible traces for which there is no input data for them to be executed. In this paper we build on the theory of workflow nets and introduce workflow nets where transitions have conditions associated with them. We then demonstrate that we can determine which execution traces, that are possible according to the control-flow dependencies, are actually possible taking the data perspective into account. This way we are able to more accurately determine in design time the infeasible traces caused by the correlation between transition conditions along this trace. Finally, we provide a solution to automatically detecting the shortest infeasible trace.
In constructing an efficient multicast tree of application-level multicast, not only the delay of link should be considered, but also the process delay of many end systems in sending and transmitting data can not be i...
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Advances in mobile networking and information processing technologies have triggered vehicular ad hoc networks (VANETs) for traffic safety and value-added applications. Most efforts have been made to address the secur...
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Enterprises outsourcing their databases to the cloud and authorizing multiple users for access represents a typical use scenario of cloud storage services. In such a case of database outsourcing, data encryption is a ...
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Certificateless cryptography eliminates the key escrow problem in identity-based cryptography. Hierarchical cryptography exploits a practical security model to mirror the organizational hierarchy in the real world. In...
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For steganalysis of JPEG images, features derived in the embedding domain appear to achieve a preferable performance. However, with the existing JPEG steganography, the minor changes due to the hidden secret data are ...
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For steganalysis of JPEG images, features derived in the embedding domain appear to achieve a preferable performance. However, with the existing JPEG steganography, the minor changes due to the hidden secret data are not easy to be explored directly from the quantized block DCT （BDCT） coefficients in that the energy of the carrier image is much larger than that of the hidden signal. In this paper, we present an improved calibration-based universal JPEG steganalysis, where the microscopic and macroscopic calibrations are combined to calibrate the local and global distribution of the quantized BDCT coefficients of the test image. All features in our method are generated from the difference signal be- tween the quantized BDCT coefficients of the test image and its corresponding microscopic calibrated image, or calculated as the difference between the signal extracted from test image and its corresponding macroscopic calibrated image. The extracted features will be more effective for our classification. Moreover, through using the Markov empirical transition matrices, both magnitude and sign dependencies along row scanning and column scanning patterns existed in intra-block and inter-block quantized BDCT coefficients are employed in our method. Experimental results demonstrate that our proposed scheme outperforms the best effective JPEG steganalyzers having been presented.
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