The Experts below are selected from a list of 18183 Experts worldwide ranked by ideXlab platform
Chukun Zhuang - One of the best experts on this subject based on the ideXlab platform.
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weight vector grid with new archive Update Mechanism for multi objective optimization
Genetic and Evolutionary Computation Conference, 2018Co-Authors: Xizi Ni, Hisao Ishibuchi, Ke Shang, Chukun ZhuangAbstract:Currently, most of the decomposition-based multi-objective evolutionary algorithms (MOEA) are based on a number of prespecified weight vectors. However, when the shape of the Pareto front is inconsistent with the distribution of weight vectors, only a small number of non-dominated solutions can be obtained inside the Pareto front. Moreover, if an external archive with a dominance-based Update Mechanism is used to overcome this difficulty, a large computational time is needed which is often unpractical. In this paper, we propose a new archive Update Mechanism with a new archive structure. A large weight vector grid is used to Update the archive by using a scalarizing function. The proposed archive Update Mechanism can be applied to any MOEA with an external archive. We examine the effectiveness of the proposed Mechanism on MOEA/D. Our experimental results show that MOEA/D with the proposed new archive Update Mechanism is able to find more solutions inside the Pareto front compared to MOEA/D without the archive. In addition, it needs less computational time compared to MOEA/D with the dominance-based archive Update Mechanism.
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GECCO (Companion) - Weight vector grid with new archive Update Mechanism for multi-objective optimization
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018Co-Authors: Hisao Ishibuchi, Ke Shang, Kanzhen Wan, Chukun ZhuangAbstract:Currently, most of the decomposition-based multi-objective evolutionary algorithms (MOEA) are based on a number of prespecified weight vectors. However, when the shape of the Pareto front is inconsistent with the distribution of weight vectors, only a small number of non-dominated solutions can be obtained inside the Pareto front. Moreover, if an external archive with a dominance-based Update Mechanism is used to overcome this difficulty, a large computational time is needed which is often unpractical. In this paper, we propose a new archive Update Mechanism with a new archive structure. A large weight vector grid is used to Update the archive by using a scalarizing function. The proposed archive Update Mechanism can be applied to any MOEA with an external archive. We examine the effectiveness of the proposed Mechanism on MOEA/D. Our experimental results show that MOEA/D with the proposed new archive Update Mechanism is able to find more solutions inside the Pareto front compared to MOEA/D without the archive. In addition, it needs less computational time compared to MOEA/D with the dominance-based archive Update Mechanism.
Timothy Baldwin - One of the best experts on this subject based on the ideXlab platform.
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on line trend analysis with topic models twitter trends detection topic model online
International Conference on Computational Linguistics, 2012Co-Authors: Nigel Collier, Timothy BaldwinAbstract:We present a novel topic modelling-based methodology to track emerging events in microblogs such as Twitter. Our topic model has an in-built Update Mechanism based on time slices and implements a dynamic vocabulary. We first show that the method is robust in detecting events using a range of datasets with injected novel events, and then demonstrate its application in identifying trending topics in Twitter.
Myong Soon Park - One of the best experts on this subject based on the ideXlab platform.
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Request distribution for fairness with a new load-Update Mechanism in Web server cluster
Lecture Notes in Computer Science, 2004Co-Authors: Myong Soon ParkAbstract:The complexity of services and applications provided by Web sites is ever increasing as integration of traditional Web publishing sites with new paradigms, i.e., e-commerce. Each dynamic Web page is difficult to estimate its execution load even with the information from the application layer. In this paper the execution latency at Web server is exploited in order to balance loads from dynamic Web pages. With only the information such as IP address and port number for Layer-4 Web switch the proposed algorithm balances the loads with a new load-Update Mechanism. The Mechanism uses the report packets more efficiently with the same communication cost. Moreover the proposed algorithm considers the fairness for Web clients hence the Web clients would experience higher quality of service.
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NPC - Request Distribution for Fairness with a New Load-Update Mechanism in Web Server Cluster
Lecture Notes in Computer Science, 2004Co-Authors: Myong Soon ParkAbstract:The complexity of services and applications provided by Web sites is ever increasing as integration of traditional Web publishing sites with new paradigms, i.e., e-commerce. Each dynamic Web page is difficult to estimate its execution load even with the information from the application layer. In this paper the execution latency at Web server is exploited in order to balance loads from dynamic Web pages. With only the information such as IP address and port number for Layer-4 Web switch the proposed algorithm balances the loads with a new load-Update Mechanism. The Mechanism uses the report packets more efficiently with the same communication cost. Moreover the proposed algorithm considers the fairness for Web clients hence the Web clients would experience higher quality of service.
Lei Shu - One of the best experts on this subject based on the ideXlab platform.
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a dynamic trust evaluation and Update Mechanism based on c4 5 decision tree in underwater wireless sensor networks
IEEE Transactions on Vehicular Technology, 2020Co-Authors: Jinfang Jiang, Mohsen Guizani, Xinyu Zhu, Guangjie Han, Lei ShuAbstract:At present, Underwater Wireless Sensor Networks (UWSNs) have been widely used in enormous applications, and simultaneously face many security threats. The trust management Mechanism plays an important role in protecting network security. Many theories, e.g., subjective logic, Bayesian, cloud model, entropy theory, evidence theory, etc., have been adopted to evaluate the node trust of wireless sensor networks. However, due to the unique characteristics of the underwater dynamic environment, the existing trust Mechanisms used in traditional networks (such as P2P networks, Ad-hoc networks, etc.) cannot be directly used in UWSNs. Therefore, this paper proposes a new trust evaluation and Update Mechanism for underwater wireless sensor networks based on the C4.5 decision tree algorithm (TEUC). In the TEUC, trust evidences are first collected including data-based, link-based and node-based trust evidences. Then, the collected trust evidences are used to train the C4.5 decision tree. In addition, the reward and penalty factors are defined to Update trust based on the sliding time window. Finally, simulation results demonstrate that the proposed algorithm outperforms the traditional ones in a dynamic network environment in terms of malicious node detection and energy consumption.
Mohsen Guizani - One of the best experts on this subject based on the ideXlab platform.
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a dynamic trust evaluation and Update Mechanism based on c4 5 decision tree in underwater wireless sensor networks
IEEE Transactions on Vehicular Technology, 2020Co-Authors: Jinfang Jiang, Mohsen Guizani, Xinyu Zhu, Guangjie Han, Lei ShuAbstract:At present, Underwater Wireless Sensor Networks (UWSNs) have been widely used in enormous applications, and simultaneously face many security threats. The trust management Mechanism plays an important role in protecting network security. Many theories, e.g., subjective logic, Bayesian, cloud model, entropy theory, evidence theory, etc., have been adopted to evaluate the node trust of wireless sensor networks. However, due to the unique characteristics of the underwater dynamic environment, the existing trust Mechanisms used in traditional networks (such as P2P networks, Ad-hoc networks, etc.) cannot be directly used in UWSNs. Therefore, this paper proposes a new trust evaluation and Update Mechanism for underwater wireless sensor networks based on the C4.5 decision tree algorithm (TEUC). In the TEUC, trust evidences are first collected including data-based, link-based and node-based trust evidences. Then, the collected trust evidences are used to train the C4.5 decision tree. In addition, the reward and penalty factors are defined to Update trust based on the sliding time window. Finally, simulation results demonstrate that the proposed algorithm outperforms the traditional ones in a dynamic network environment in terms of malicious node detection and energy consumption.
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BLLC: A Batch-Level Update Mechanism With Low Cost for SDN-IoT Networks
IEEE Internet of Things Journal, 2019Co-Authors: Wei Ren, Yan Sun, Hong Luo, Mohsen GuizaniAbstract:Software defined networks have been a driving force of the Internet of Things (IoT) advancement in the devices management and network control. One of the important challenges remaining to be resolved is how to safely complete the network Update with low resources’ consumption. In this paper, we propose a new batch-level Update Mechanism with low cost (BLLC) to solve this problem. Its main idea is to bundle control commands and Update networks from the destination to the source of new flows. Specifically, we first build Update trees for all new flows according to the network states and consistency properties. Then, the new rules in an Update tree are packaged into a single control packet (UBCP). Furthermore, a virtual destination-based algorithm is devised to minimize the number of finally formed UBCPs. The generated UBCPs will successively pass all IoT nodes to be Updated, and instruct them to apply the required operations. If the control channel is wireless, we introduce a cooperative transmission Mechanism to enhance the reliability of the UBCPs’ forwarding. Finally, we evaluate our scheme by comprehensive experiments. The results show that the BLLC reduces the link cost by 68% on the average with a slight performance loss in the Update time.
