The Experts below are selected from a list of 156 Experts worldwide ranked by ideXlab platform

Lizhen Cui - One of the best experts on this subject based on the ideXlab platform.

  • SCC - A Pre-joined Service Composition Approach with Dynamic Services in a Graph Database
    2020 IEEE International Conference on Services Computing (SCC), 2020
    Co-Authors: Ming Zhu, Yuhong Yan, Lizhen Cui
    Abstract:

    Database-based Composition approaches are evolving and coming into researchers’ notice. In this paper, we present a pre-joined service Composition approach with dynamic services in a Graph database. Firstly, services’ information is stored in a graph database. Secondly, a Composition Network is constructed. Last but not least, a solution by converting the Composition problem into graph database queries is fetched. Considering that services are dynamic and lead to frequent changes of service Compositions, we discuss how to update the graph database, e.g., service addition, disappearance and update. Preliminary experiment indicates that the proposed approach can support service Composition with services’ dynamic changes.

  • Service Composition based on pre-joined service Network in graph database
    International Journal of Web and Grid Services, 2020
    Co-Authors: Ming Zhu, Yuhong Yan, Lizhen Cui
    Abstract:

    To perform an arbitrary service Composition task in a graph database, and to support plug-in semantic matching of services, we present a novel service Composition approach in a graph database named pre-joined service Network. Firstly, the proposed approach constructs and stores a Composition Network with all services and Compositions in a graph database. Then, it fetches a satisfying solution by converting the user's request into queries in the graph database. To illustrate the process of searching for a solution, a simple but meaningful example is provided. Furthermore, we test the performance of the proposed approach with a challenge dataset. Experiment results show that the proposed approach can always find a valid solution and lead to higher user satisfaction when compared with the pre-joined semantic indexing graph approach.

Ming Zhu - One of the best experts on this subject based on the ideXlab platform.

  • SCC - A Pre-joined Service Composition Approach with Dynamic Services in a Graph Database
    2020 IEEE International Conference on Services Computing (SCC), 2020
    Co-Authors: Ming Zhu, Yuhong Yan, Lizhen Cui
    Abstract:

    Database-based Composition approaches are evolving and coming into researchers’ notice. In this paper, we present a pre-joined service Composition approach with dynamic services in a Graph database. Firstly, services’ information is stored in a graph database. Secondly, a Composition Network is constructed. Last but not least, a solution by converting the Composition problem into graph database queries is fetched. Considering that services are dynamic and lead to frequent changes of service Compositions, we discuss how to update the graph database, e.g., service addition, disappearance and update. Preliminary experiment indicates that the proposed approach can support service Composition with services’ dynamic changes.

  • Service Composition based on pre-joined service Network in graph database
    International Journal of Web and Grid Services, 2020
    Co-Authors: Ming Zhu, Yuhong Yan, Lizhen Cui
    Abstract:

    To perform an arbitrary service Composition task in a graph database, and to support plug-in semantic matching of services, we present a novel service Composition approach in a graph database named pre-joined service Network. Firstly, the proposed approach constructs and stores a Composition Network with all services and Compositions in a graph database. Then, it fetches a satisfying solution by converting the user's request into queries in the graph database. To illustrate the process of searching for a solution, a simple but meaningful example is provided. Furthermore, we test the performance of the proposed approach with a challenge dataset. Experiment results show that the proposed approach can always find a valid solution and lead to higher user satisfaction when compared with the pre-joined semantic indexing graph approach.

Xiaojie Guo - One of the best experts on this subject based on the ideXlab platform.

  • single image rain removal via a deep deComposition Composition Network
    Computer Vision and Image Understanding, 2019
    Co-Authors: Wenqi Ren, Jiawan Zhang, Xiaojie Guo
    Abstract:

    Abstract Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel multi-task leaning architecture in an end-to-end manner to reduce the mapping range from input to output and boost the performance. Concretely, a deComposition net is built to split rain images into clean background and rain layers. Different from previous architectures, our model consists of, besides a component representing the desired clean image, an extra component for the rain layer. During the training phase, we further employ a Composition structure to reproduce the input by the separated clean image and rain information for improving the quality of deComposition. Experimental results on both synthetic and real images are conducted to reveal the high-quality recovery by our design, and show its superiority over other state-of-the-art methods. Furthermore, our design is also applicable to other layer deComposition tasks like dust removal. More importantly, our method only requires about 50ms to process a testing image in VGA resolution on a GTX 1080 GPU with promising rain removal quality, making it attractive for practical use. The synthesized dataset and code are publicly available at https://sites.google.com/view/xjguo/rain .

  • Single image rain removal via a deep deComposition–Composition Network
    Computer Vision and Image Understanding, 2019
    Co-Authors: Wenqi Ren, Jiawan Zhang, Xiaojie Guo
    Abstract:

    Abstract Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel multi-task leaning architecture in an end-to-end manner to reduce the mapping range from input to output and boost the performance. Concretely, a deComposition net is built to split rain images into clean background and rain layers. Different from previous architectures, our model consists of, besides a component representing the desired clean image, an extra component for the rain layer. During the training phase, we further employ a Composition structure to reproduce the input by the separated clean image and rain information for improving the quality of deComposition. Experimental results on both synthetic and real images are conducted to reveal the high-quality recovery by our design, and show its superiority over other state-of-the-art methods. Furthermore, our design is also applicable to other layer deComposition tasks like dust removal. More importantly, our method only requires about 50ms to process a testing image in VGA resolution on a GTX 1080 GPU with promising rain removal quality, making it attractive for practical use. The synthesized dataset and code are publicly available at https://sites.google.com/view/xjguo/rain .

  • Fast Single Image Rain Removal via a Deep DeComposition-Composition Network.
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Wenqi Ren, Jiawan Zhang, Xiaojie Guo
    Abstract:

    Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel multi-task leaning architecture in an end-to-end manner to reduce the mapping range from input to output and boost the performance. Concretely, a deComposition net is built to split rain images into clean background and rain layers. Different from previous architectures, our model consists of, besides a component representing the desired clean image, an extra component for the rain layer. During the training phase, we further employ a Composition structure to reproduce the input by the separated clean image and rain information for improving the quality of deComposition. Experimental results on both synthetic and real images are conducted to reveal the high-quality recovery by our design, and show its superiority over other state-of-the-art methods. Furthermore, our design is also applicable to other layer deComposition tasks like dust removal. More importantly, our method only requires about 50ms, significantly faster than the competitors, to process a testing image in VGA resolution on a GTX 1080 GPU, making it attractive for practical use.

Yuhong Yan - One of the best experts on this subject based on the ideXlab platform.

  • SCC - A Pre-joined Service Composition Approach with Dynamic Services in a Graph Database
    2020 IEEE International Conference on Services Computing (SCC), 2020
    Co-Authors: Ming Zhu, Yuhong Yan, Lizhen Cui
    Abstract:

    Database-based Composition approaches are evolving and coming into researchers’ notice. In this paper, we present a pre-joined service Composition approach with dynamic services in a Graph database. Firstly, services’ information is stored in a graph database. Secondly, a Composition Network is constructed. Last but not least, a solution by converting the Composition problem into graph database queries is fetched. Considering that services are dynamic and lead to frequent changes of service Compositions, we discuss how to update the graph database, e.g., service addition, disappearance and update. Preliminary experiment indicates that the proposed approach can support service Composition with services’ dynamic changes.

  • Service Composition based on pre-joined service Network in graph database
    International Journal of Web and Grid Services, 2020
    Co-Authors: Ming Zhu, Yuhong Yan, Lizhen Cui
    Abstract:

    To perform an arbitrary service Composition task in a graph database, and to support plug-in semantic matching of services, we present a novel service Composition approach in a graph database named pre-joined service Network. Firstly, the proposed approach constructs and stores a Composition Network with all services and Compositions in a graph database. Then, it fetches a satisfying solution by converting the user's request into queries in the graph database. To illustrate the process of searching for a solution, a simple but meaningful example is provided. Furthermore, we test the performance of the proposed approach with a challenge dataset. Experiment results show that the proposed approach can always find a valid solution and lead to higher user satisfaction when compared with the pre-joined semantic indexing graph approach.

Pao-lien Lai - One of the best experts on this subject based on the ideXlab platform.

  • The two-equal-disjoint path cover problem of Matching Composition Network
    Information Processing Letters, 2008
    Co-Authors: Pao-lien Lai, Hong-chun Hsu
    Abstract:

    Embedding of paths have attracted much attention in the parallel processing. Many-to-many communication is one of the most central issues in various interconnection Networks. A graph G is globally two-equal-disjoint path coverable if for any two distinct pairs of vertices (u,v) and (w,x) of G, there exist two disjoint paths P and Q satisfied that (1) P (Q, respectively) joins u and v (w and x, respectively), (2) |P|=|Q|, and (3) V([email protected]?Q)=V(G). The Matching Composition Network (MCN) is a family of Networks which two components are connected by a perfect matching. In this paper, we consider the globally two-equal-disjoint path cover property of MCN. Applying our result, the Crossed cube CQ"n, the Twisted cube TQ"n, and the Mobius cube MQ"n can all be proven to be globally two-equal-disjoint path coverable for n>=5.

  • The diagnosability of the matching Composition Network under the comparison diagnosis model
    IEEE Transactions on Computers, 2004
    Co-Authors: Pao-lien Lai, Jimmy J. M. Tan, Chang-hsiung Tsai, Lih-hsing Hsu
    Abstract:

    The classical problem of diagnosability is discussed widely and the diagnosability of many well-known Networks has been explored. We consider the diagnosability of a family of Networks, called the matching Composition Network (MCN); a perfect matching connects two components. The diagnosability of MCN under the comparison model is shown to be one larger than that of the component, provided some connectivity constraints are satisfied. Applying our result, the diagnosability of the hypercube Qn, the crossed cube CQ/sub n/, the twisted cube TQ/sub n/, and the Mobius cube MQ/sub n/ can all proven to be n, for n/spl ges/4. In particular, we show that the diagnosability of the four-dimensional hypercube Q/sub 4/ is 4, which is not previously known.