The Experts below are selected from a list of 8148 Experts worldwide ranked by ideXlab platform
Ling Gao - One of the best experts on this subject based on the ideXlab platform.
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hierarchical learning of tree classifiers for large scale plant species identification
IEEE Transactions on Image Processing, 2015Co-Authors: Jianping Fan, Jinye Peng, Ning Zhou, Ling GaoAbstract:In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given Parent Node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a Parent Node (high-level non-leaf Node) correctly if it can further be assigned to the most relevant child Node (low-level non-leaf Node or leaf Node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.
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ICSC - Hierarchical learning of tree classifiers for large-scale plant species identification
Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), 2015Co-Authors: Jianping Fan, Jinye Peng, Ling Gao, Ning ZhouAbstract:A hierarchical learning algorithm is developed for supporting large-scale plant species identification. A visual tree is first constructed for organizing large numbers of plant species hierarchically in a coarse-to-fine fashion. For the fine-grained plant species at the sibling leaf Nodes under the same Parent Node, they share significant common visual properties but still contain subtle visual differences, a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. For the coarse-grained categories at the sibling non-leaf Nodes under the same Parent Node, a hierarchical classifier training algorithm is developed to leverage both the tree structure (i.e., inter-level constraint) and the common prediction structures shared among their sibling child Nodes (i.e., inter-level visual correlation) to train their inter-related classifiers hierarchically. Our experimental results on large-scale plant images have demonstrated the effectiveness of our algorithm on large-scale plant species identification.
Willy Herroelen - One of the best experts on this subject based on the ideXlab platform.
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A Branch-and-Bound Procedure for the Generalized Resource-Constrained Project Scheduling Problem
Operations Research, 1997Co-Authors: Erik Demeulemeester, Willy HerroelenAbstract:In this paper a branch-and-bound procedure is described for scheduling project activities subject to precedence diagramming type of precedence relations, ready times, due dates, and variable multiple resource availability constraints, where the objective is to minimize project duration. The procedure is based on a depth-first solution strategy in which Nodes in the solution tree represent resource and precedence feasible partial schedules. Branches emanating from a Parent Node correspond to exhaustive and minimal combinations of activities, the delay of which resolves resource conflicts at each Parent Node. A precedence based lower bound and several dominance rules are introduced in order to restrict the growth of the solutions tree. The procedure has been programmed in the C language. Extensive computational experience is reported.
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an efficient optimal solution procedure for the preemptive resource constrained project scheduling problem
European Journal of Operational Research, 1996Co-Authors: Erik Demeulemeester, Willy HerroelenAbstract:Abstract In this paper a branch-and-bound procedure is described for scheduling project activities subject to precedence and resource constraints, where activities can be preempted at any discrete time instant and where the objective is to minimize the project duration. The procedure is based on a depth-first solution strategy in which Nodes in the solution tree represent resource and precedence feasible partial schedules. Branches emanating from a Parent Node correspond to exhaustive and minimal combinations of activities, the delay of which resolves resource conflicts at each Parent Node. A precedence based lower bound and several dominance rules are introduced in order to restrict the growth of the solutions tree. The solution procedure has been programmed in the C language and extensive computational experience is reported.
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a branch and bound procedure for the multiple resource constrained project scheduling problem
Management Science, 1992Co-Authors: Erik Demeulemeester, Willy HerroelenAbstract:In this paper a branch-and-bound procedure is described for scheduling the activities of a project of the PERT/CPM variety subject to precedence and resource constraints where the objective is to minimize project duration. The procedure is based on a depth-first solution strategy in which Nodes in the solution tree represent resource and precedence feasible partial schedules. Branches emanating from a Parent Node correspond to exhaustive and minimal combinations of activities, the delay of which resolves resource conflicts at each Parent Node. Precedence and resource-based bounds described in the paper are combined with new dominance pruning rules to rapidly fathom major portions of the solution tree. The procedure is programmed in the C language for use on both a mainframe and a personal computer. The procedure has been validated using a standard set of test problems with between 7 and 50 activities requiring up to three resource types each. Computational experience on a personal computer indicates that the procedure is 11.6 times faster than the most rapid solution procedure reported in the literature while requiring less computer storage. Moreover, problems requiring large amounts of computer time using existing approaches for solving this problem type are rapidly solved with our procedure using the dominance rules described, resulting in a significant reduction in the variability in solution times as well.
Jianping Fan - One of the best experts on this subject based on the ideXlab platform.
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hierarchical learning of tree classifiers for large scale plant species identification
IEEE Transactions on Image Processing, 2015Co-Authors: Jianping Fan, Jinye Peng, Ning Zhou, Ling GaoAbstract:In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given Parent Node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a Parent Node (high-level non-leaf Node) correctly if it can further be assigned to the most relevant child Node (low-level non-leaf Node or leaf Node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.
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ICSC - Hierarchical learning of tree classifiers for large-scale plant species identification
Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), 2015Co-Authors: Jianping Fan, Jinye Peng, Ling Gao, Ning ZhouAbstract:A hierarchical learning algorithm is developed for supporting large-scale plant species identification. A visual tree is first constructed for organizing large numbers of plant species hierarchically in a coarse-to-fine fashion. For the fine-grained plant species at the sibling leaf Nodes under the same Parent Node, they share significant common visual properties but still contain subtle visual differences, a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. For the coarse-grained categories at the sibling non-leaf Nodes under the same Parent Node, a hierarchical classifier training algorithm is developed to leverage both the tree structure (i.e., inter-level constraint) and the common prediction structures shared among their sibling child Nodes (i.e., inter-level visual correlation) to train their inter-related classifiers hierarchically. Our experimental results on large-scale plant images have demonstrated the effectiveness of our algorithm on large-scale plant species identification.
Iwao Sasase - One of the best experts on this subject based on the ideXlab platform.
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secure Parent Node selection scheme in route construction to exclude attacking Nodes from rpl network
Asia-Pacific Conference on Communications, 2015Co-Authors: Kenji Iuchi, Takumi Matsunaga, Kentaroh Toyoda, Iwao SasaseAbstract:The IPv6 Routing Protocol for Low-power and Lossy networks (RPL) is a standard routing protocol to realize the Internet of Things (IoT). Since RPL is a tree-based topology network, an attacking Node may falsely claim its rank towards neighbor Nodes in order to be chosen as a Parent of them and to collect more packets to tamper. In this paper, we propose a secure Parent selection scheme so that each child Node can select a legitimate Node as its Parent. In the proposed scheme, each Node chooses a Parent after excluding the best candidate if multiple Parent candidates exist. Our scheme utilizes the fact that an attacking Node claims falsely a lower rank than that of a legitimate Nodes. We show that attacking Nodes have no merits to claim lower ranks than true ones in a secure Parent Node selection scheme. By the computer simulation, we show that the proposed scheme reduces the total number of child Nodes attached to attacking Nodes in comparison with the conventional RPL scheme.
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APCC - Secure Parent Node selection scheme in route construction to exclude attacking Nodes from RPL network
2015 21st Asia-Pacific Conference on Communications (APCC), 2015Co-Authors: Kenji Iuchi, Takumi Matsunaga, Kentaroh Toyoda, Iwao SasaseAbstract:The IPv6 Routing Protocol for Low-power and Lossy networks (RPL) is a standard routing protocol to realize the Internet of Things (IoT). Since RPL is a tree-based topology network, an attacking Node may falsely claim its rank towards neighbor Nodes in order to be chosen as a Parent of them and to collect more packets to tamper. In this paper, we propose a secure Parent selection scheme so that each child Node can select a legitimate Node as its Parent. In the proposed scheme, each Node chooses a Parent after excluding the best candidate if multiple Parent candidates exist. Our scheme utilizes the fact that an attacking Node claims falsely a lower rank than that of a legitimate Nodes. We show that attacking Nodes have no merits to claim lower ranks than true ones in a secure Parent Node selection scheme. By the computer simulation, we show that the proposed scheme reduces the total number of child Nodes attached to attacking Nodes in comparison with the conventional RPL scheme.
Erik Demeulemeester - One of the best experts on this subject based on the ideXlab platform.
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A Branch-and-Bound Procedure for the Generalized Resource-Constrained Project Scheduling Problem
Operations Research, 1997Co-Authors: Erik Demeulemeester, Willy HerroelenAbstract:In this paper a branch-and-bound procedure is described for scheduling project activities subject to precedence diagramming type of precedence relations, ready times, due dates, and variable multiple resource availability constraints, where the objective is to minimize project duration. The procedure is based on a depth-first solution strategy in which Nodes in the solution tree represent resource and precedence feasible partial schedules. Branches emanating from a Parent Node correspond to exhaustive and minimal combinations of activities, the delay of which resolves resource conflicts at each Parent Node. A precedence based lower bound and several dominance rules are introduced in order to restrict the growth of the solutions tree. The procedure has been programmed in the C language. Extensive computational experience is reported.
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an efficient optimal solution procedure for the preemptive resource constrained project scheduling problem
European Journal of Operational Research, 1996Co-Authors: Erik Demeulemeester, Willy HerroelenAbstract:Abstract In this paper a branch-and-bound procedure is described for scheduling project activities subject to precedence and resource constraints, where activities can be preempted at any discrete time instant and where the objective is to minimize the project duration. The procedure is based on a depth-first solution strategy in which Nodes in the solution tree represent resource and precedence feasible partial schedules. Branches emanating from a Parent Node correspond to exhaustive and minimal combinations of activities, the delay of which resolves resource conflicts at each Parent Node. A precedence based lower bound and several dominance rules are introduced in order to restrict the growth of the solutions tree. The solution procedure has been programmed in the C language and extensive computational experience is reported.
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a branch and bound procedure for the multiple resource constrained project scheduling problem
Management Science, 1992Co-Authors: Erik Demeulemeester, Willy HerroelenAbstract:In this paper a branch-and-bound procedure is described for scheduling the activities of a project of the PERT/CPM variety subject to precedence and resource constraints where the objective is to minimize project duration. The procedure is based on a depth-first solution strategy in which Nodes in the solution tree represent resource and precedence feasible partial schedules. Branches emanating from a Parent Node correspond to exhaustive and minimal combinations of activities, the delay of which resolves resource conflicts at each Parent Node. Precedence and resource-based bounds described in the paper are combined with new dominance pruning rules to rapidly fathom major portions of the solution tree. The procedure is programmed in the C language for use on both a mainframe and a personal computer. The procedure has been validated using a standard set of test problems with between 7 and 50 activities requiring up to three resource types each. Computational experience on a personal computer indicates that the procedure is 11.6 times faster than the most rapid solution procedure reported in the literature while requiring less computer storage. Moreover, problems requiring large amounts of computer time using existing approaches for solving this problem type are rapidly solved with our procedure using the dominance rules described, resulting in a significant reduction in the variability in solution times as well.
