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Self-healing key distribution for large-scale wireless sensor networks 大规模无线传感器网络的自愈密钥分配
Third International Workshop on Advanced Computational Intelligence Pub Date : 2010-09-23 DOI: 10.1109/IWACI.2010.5585114
Qiuhua Wang
{"title":"Self-healing key distribution for large-scale wireless sensor networks","authors":"Qiuhua Wang","doi":"10.1109/IWACI.2010.5585114","DOIUrl":"https://doi.org/10.1109/IWACI.2010.5585114","url":null,"abstract":"Recently, T. Yuan et al. proposed a self-healing key distribution with revocation and collusion resistance for wireless sensor networks based on the revocation and participation polynomials. However, we show that T. Yuan's scheme is insecure against the proposed attack. In this paper, we modify the T. Yuan's scheme and further propose a secure and efficient self-healing key distribution scheme to overcome the flaw of T. Yuan's scheme. Our proposed scheme reduces the user's storage overhead to a constant value 2logq bits, and makes the communication overhead be also optimal. Moreover, we analyze our scheme in an appropriate security model and prove that it is unconditionally secure and not only achieves forward and backward secrecy, but also resists to the collusion attack among the revoked users and the new joined users.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133769743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Moving targets detection and tracking based on Bayesian foreground segmentation and GVF-snake 基于贝叶斯前景分割和GVF-snake的运动目标检测与跟踪
Third International Workshop on Advanced Computational Intelligence Pub Date : 2010-09-23 DOI: 10.1109/IWACI.2010.5585122
Changjun Wang, Guojun Dai
{"title":"Moving targets detection and tracking based on Bayesian foreground segmentation and GVF-snake","authors":"Changjun Wang, Guojun Dai","doi":"10.1109/IWACI.2010.5585122","DOIUrl":"https://doi.org/10.1109/IWACI.2010.5585122","url":null,"abstract":"We proposed a robust approach to detect and track moving targets observed by a static camera. The approach relies on a Bayes theorem based background model, a GVF-snake based border tracker and a Kalman estimator. The background model is used to segment foreground targets from background, which has the advantages of insensitiveness to initial observations and the capability of adaptive selection of layer number compared with GMM background model. By modifying its energy term and adding automatic initialization of contours, GVF snake is improved to extract the contours of moving targets in video. To speed up convergence, we introduced a Kalman filter to estimate the contour centers. We demonstrated results on a number of different real sequences. The proposed method was proved effective for both rigid and non-rigid objects and can be used for smart surveillance and traffic monitoring.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128877624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Experimental study on PSO diversity PSO多样性的实验研究
Third International Workshop on Advanced Computational Intelligence Pub Date : 2010-09-23 DOI: 10.1109/IWACI.2010.5585208
Zhi-hui Zhan, Jun Zhang, Yu-hui Shi
{"title":"Experimental study on PSO diversity","authors":"Zhi-hui Zhan, Jun Zhang, Yu-hui Shi","doi":"10.1109/IWACI.2010.5585208","DOIUrl":"https://doi.org/10.1109/IWACI.2010.5585208","url":null,"abstract":"Particle swarm optimization (PSO) has been witnessed fast developments these years for the algorithm performance improvements and the applications in real-world problems. However, the experimental study on the population diversities is not taken seriously by the PSO researchers. This paper intends to make a comprehensive experimental study on the PSO diversity, in order to monitor the evolutionary process of the PSO algorithms, and also to give some discussions based on the observations of the experimental results.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130620064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
A predictor from numerical data based on fuzzy sets and rough sets 基于模糊集和粗糙集的数值数据预测器
Third International Workshop on Advanced Computational Intelligence Pub Date : 2010-09-23 DOI: 10.1109/IWACI.2010.5585153
Chih-Ching Hsiao, Yi-Wei Ku
{"title":"A predictor from numerical data based on fuzzy sets and rough sets","authors":"Chih-Ching Hsiao, Yi-Wei Ku","doi":"10.1109/IWACI.2010.5585153","DOIUrl":"https://doi.org/10.1109/IWACI.2010.5585153","url":null,"abstract":"In this paper, we propose a fuzzy predictor which fuzzy rules are generated directly from numerical data pairs. Unfortunately, the fuzzy rules may be increase growing to extra numbers, especially the data pairs contain noise or outlier. The Fuzzy-rough feature selection will be introduced for those fuzzy rules reduction. To achieve good performance for this fuzzy predictor, the parameters of each fuzzy rule will be adjusted by fine tuning.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132913008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Evolving agent-based simulations in the clouds 云中基于智能体的模拟进化
Third International Workshop on Advanced Computational Intelligence Pub Date : 2010-09-23 DOI: 10.1109/IWACI.2010.5585192
James Decraene, Y. Cheng, M. Low, Suiping Zhou, Wentong Cai, Chwee Seng Choo
{"title":"Evolving agent-based simulations in the clouds","authors":"James Decraene, Y. Cheng, M. Low, Suiping Zhou, Wentong Cai, Chwee Seng Choo","doi":"10.1109/IWACI.2010.5585192","DOIUrl":"https://doi.org/10.1109/IWACI.2010.5585192","url":null,"abstract":"Evolving agent-based simulations enables one to automate the difficult iterative process of modeling complex adaptive systems to exhibit pre-specified/desired behaviors. Nevertheless this emerging technology, combining research advances in agent-based modeling/simulation and evolutionary computation, requires significant computing resources (i.e., high performance computing facilities) to evaluate simulation models across a large search space. Moreover, such experiments are typically conducted in an infrequent fashion and may occur when the computing facilities are not fully available. The user may thus be confronted with a computing budget limiting the use of these “evolvable simulation” techniques. We propose the use of the cloud computing paradigm to address these budget and flexibility issues. To assist this research, we utilize a modular evolutionary framework coined CASE (for complex adaptive system evolver) which is capable of evolving agent-based models using nature-inspired search algorithms. In this paper, we present an adaptation of this framework which supports the cloud computing paradigm. An example evolutionary experiment, which examines a simplified military scenario modeled with the agent-based simulation platform MANA, is presented. This experiment refers to Automated Red Teaming: a vulnerability assessment tool employed by defense analysts to study combat operations (which are regarded here as complex adaptive systems). The experimental results suggest promising research potential in exploiting the cloud computing paradigm to support computing intensive evolvable simulation experiments. Finally, we discuss an additional extension to our cloud computing compliant CASE in which we propose to incorporate a distributed evolutionary approach, e.g., the island-based model to further optimize the evolutionary search.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134647270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 17
A fast learning algorithm for principal component extraction with data dependent learning rate 基于数据依赖学习率的主成分提取快速学习算法
Third International Workshop on Advanced Computational Intelligence Pub Date : 2010-09-23 DOI: 10.1109/IWACI.2010.5585143
Lijun Liu, Rendong Ge, Jun Tie
{"title":"A fast learning algorithm for principal component extraction with data dependent learning rate","authors":"Lijun Liu, Rendong Ge, Jun Tie","doi":"10.1109/IWACI.2010.5585143","DOIUrl":"https://doi.org/10.1109/IWACI.2010.5585143","url":null,"abstract":"We propose a fast adaptive learning algorithm for computing principal eigenvector of covariance matrix arisen in the field of signal processing, where the learning process has to be repeated in online manner. Compared with most existing neural algorithms, the proposed approach effectively makes use of the online estimation of eigenvalue to update the principal eigenvector, which makes the method works with an adaptive data dependent learning rate and thus demonstrates a fast convergence speed. Numerical experiment further shows that this data dependent learning rate in the proposed algorithm offers significant advantages over that of constant learning algorithm.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114212580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Continuous lattices of L-sets l集的连续格
Third International Workshop on Advanced Computational Intelligence Pub Date : 2010-09-23 DOI: 10.1109/IWACI.2010.5585193
Xueyou Chen
{"title":"Continuous lattices of L-sets","authors":"Xueyou Chen","doi":"10.1109/IWACI.2010.5585193","DOIUrl":"https://doi.org/10.1109/IWACI.2010.5585193","url":null,"abstract":"The purpose of the paper is to introduce the notions of directed sets, continuous lattices, and algebraic lattices in fuzzy setting, and to obtain some equivalence conditions.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114397925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Stabilization of complex network with LMI impulsive control 基于LMI脉冲控制的复杂网络镇定
Third International Workshop on Advanced Computational Intelligence Pub Date : 2010-09-23 DOI: 10.1109/IWACI.2010.5585112
Lanping Chen, Zhenghua Ma, Suolin Duan
{"title":"Stabilization of complex network with LMI impulsive control","authors":"Lanping Chen, Zhenghua Ma, Suolin Duan","doi":"10.1109/IWACI.2010.5585112","DOIUrl":"https://doi.org/10.1109/IWACI.2010.5585112","url":null,"abstract":"This paper proposes an impulsive control based on linear matrix inequalities (LMI) techniques to stabilize a class of complex dynamical networks system with parameter uncertainties. The model of impulsive controlled complex network with different dynamical nodes is presented, and various robust stability conditions in the form of Linear matrix inequality(LMI) are derived for this model. Simulation results verify the validity of the proposed methodology.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114685426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Enhancing Particle Swarm Optimization via probabilistic models 基于概率模型的粒子群优化
Third International Workshop on Advanced Computational Intelligence Pub Date : 2010-09-23 DOI: 10.1109/IWACI.2010.5585216
Fang Du, Yanjun Li, Tiejun Wu
{"title":"Enhancing Particle Swarm Optimization via probabilistic models","authors":"Fang Du, Yanjun Li, Tiejun Wu","doi":"10.1109/IWACI.2010.5585216","DOIUrl":"https://doi.org/10.1109/IWACI.2010.5585216","url":null,"abstract":"Particle Swarm Optimization (PSO) has gained much success particularly in continuous optimization. However, like other black box optimizations, PSO lacks an explicit mechanism for exploiting problem specific interactions among variables, which is crucial for discouraging premature convergence. In this paper, we propose two strategies to enhance PSO via probabilistic models. Firstly, we exploit problem structures in PSO to repel premature convergence, where problem specific interactions among variables are represented as a mixture of multivariate normal distributions. Secondly, the authors propose a hybrid constraint handling method for PSO via combining “feasibility and dominance” (FAD) rules with sampling from a mixture of Truncated Multivariate Normal Distributions (mixed TMNDs), where the constraints are restricted to linear inequalities and represented as mixed TMNDs. Results for test problems indicate that the proposed enhancements significantly improve the performance of PSO.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128631990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A relevance feedback scheme based on Hidden Markov Model Regression for 3D model retrieval 基于隐马尔可夫模型回归的三维模型检索相关反馈方案
Third International Workshop on Advanced Computational Intelligence Pub Date : 2010-09-23 DOI: 10.1109/IWACI.2010.5585204
Zhi-yong Zhang, Bai Yang
{"title":"A relevance feedback scheme based on Hidden Markov Model Regression for 3D model retrieval","authors":"Zhi-yong Zhang, Bai Yang","doi":"10.1109/IWACI.2010.5585204","DOIUrl":"https://doi.org/10.1109/IWACI.2010.5585204","url":null,"abstract":"Relevance feedback is an iterative search technique to bridge the semantic gap between the high level user intention and low level data representation. This technique interactively determines a user's desired output or query concept by asking the user whether certain proposed 3D models are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately. In this paper, we propose a relevance feedback framework based on Hidden Markov Model Regression (HMMR) in content-based 3D model retrieval systems. Given a 3D model retrieval system, we collect and store user's feedback and use HMMR to enhance the retrieval performances. Experimental results show that this algorithm achieves higher search accuracy than traditional query refinement schemes.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116062520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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