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2017 13th International Conference on Computational Intelligence and Security (CIS)最新文献

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Research on Information Propagation Method Based on Individual User Characteristics 基于个人用户特征的信息传播方法研究
2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00103
Lejun Zhang, Weijie Zhao, Chunhui Zhao
{"title":"Research on Information Propagation Method Based on Individual User Characteristics","authors":"Lejun Zhang, Weijie Zhao, Chunhui Zhao","doi":"10.1109/CIS.2017.00103","DOIUrl":"https://doi.org/10.1109/CIS.2017.00103","url":null,"abstract":"At present, the research of information dissemination model based on social platform mainly focuses on the influence of social network structure and information content on information dissemination, but it is not comprehensive enough for user characteristics. And all users use the same prediction model, which will lead to the prediction results of different users will appear homogeneity. This paper focuses on the microblogging forwarding will be affected by what the individual characteristics, and then uses each user's history data to generate an independent prediction model for each user. For users with insufficient historical information data, this paper proposes a scheme to predict microblogging forwarding behavior by neighboring friends.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129842467","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
BPSOBDE: A Binary Version of Hybrid Heuristic Algorithm for Multidimensional Knapsack Problems 多维背包问题的二元混合启发式算法
2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00020
Li Zhang, Hong Li
{"title":"BPSOBDE: A Binary Version of Hybrid Heuristic Algorithm for Multidimensional Knapsack Problems","authors":"Li Zhang, Hong Li","doi":"10.1109/CIS.2017.00020","DOIUrl":"https://doi.org/10.1109/CIS.2017.00020","url":null,"abstract":"A hybrid heuristic algorithm, named BPSOBDE, is proposed for solving multidimensional knapsack problems (MKPs), in which the basic binary particle swarm optimization (BPSO) is combined with a binary differential evolution (BDE) to maintain the diversity of the swarm and makes it more explorative, effective and efficient. BPSOBDE is tested through computational experiments over suites of benchmark problems and the obtained results are compared with those of BPSO and some modified versions of BPSO. The experimental results show that BPSOBDE can successfully locate the exact solutions of all test problems. The comparison results indicate that BPSOBDE is a competitive heuristic algorithm.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127757112","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
Credit Risk Assessment Algorithm Using Deep Neural Networks with Clustering and Merging 基于聚类和合并的深度神经网络信用风险评估算法
2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00045
Ying Li, Xianghong Lin, Xiangwen Wang, Fanqi Shen, Zuzheng Gong
{"title":"Credit Risk Assessment Algorithm Using Deep Neural Networks with Clustering and Merging","authors":"Ying Li, Xianghong Lin, Xiangwen Wang, Fanqi Shen, Zuzheng Gong","doi":"10.1109/CIS.2017.00045","DOIUrl":"https://doi.org/10.1109/CIS.2017.00045","url":null,"abstract":"A reliable assessment model can help financial institutions to increase profits and reduce losses. In credit data, classes of the data are extremely imbalanced owing to the small sample size of bad customers. In this paper, we propose a credit risk assessment algorithm using deep neural networks with clustering and merging, to achieve a balanced dataset and judge whether customer can be granted loans. In the algorithm, the majority class samples are divided into several subgroups by k-means clustering algorithm, each subgroup is merged with the minority class samples to produce several balanced subgroups, and these balanced subgroups are classified using deep neural networks respectively. In the experiments, we analyze influences of the model parameters and data sampling methods on the model performance, and compare classification ability of different models. The experimental results show that the proposed algorithm has a higher prediction accuracy in credit risk assessment.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127880660","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: 11
A New Filled Function Method for the Non-convex Global Optimization 一种新的非凸全局优化的填充函数方法
2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00055
B. Qiao, Le Gao, Fei Wei
{"title":"A New Filled Function Method for the Non-convex Global Optimization","authors":"B. Qiao, Le Gao, Fei Wei","doi":"10.1109/CIS.2017.00055","DOIUrl":"https://doi.org/10.1109/CIS.2017.00055","url":null,"abstract":"The conundrum of the non-convex global optimization is that there are multiple local minima which are not global optimal solution, and conventional algorithms drop into local optimum easily. Filled function method is an available way which generally invokes an auxiliary function to move successively from a local minimizer to another better one. Definition-based on filled function, a new filled function with easy- adjustment parameter, simple form and non- exponential is proposed, and then prove the filled function can maintain the padding properties. Moreover, a local search method with stochastic and uniformity strategy is designed to strengthen the local search. Based on the above, a new filled function algorithm is presented. The numerical results indicate the proposed algorithm is feasible and effective, it follows that the stochastic and uniform strategy design is valid, further, the analysis and comparison of numerical experiments manifest high-efficiency, good stability and easy-realization.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115312266","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
On Gradient Weighting Based Fuzzy Clustering for Graph Data 基于梯度加权的图数据模糊聚类
2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00042
Shihu Liu, Liping Jia, Fusheng Yu
{"title":"On Gradient Weighting Based Fuzzy Clustering for Graph Data","authors":"Shihu Liu, Liping Jia, Fusheng Yu","doi":"10.1109/CIS.2017.00042","DOIUrl":"https://doi.org/10.1109/CIS.2017.00042","url":null,"abstract":"This paper introduce a gradient weighting based fuzzy clustering algorithm for graph data, in which the clustering process can be regarded as an optimization for objective function. During the process of iteration, the partition matrix is updated by a convex combination of partition information with respect to attribute information and the closeness information between partition information and relational information. On these bases, the iteration process is constructed with the help of fuzzy c-means clustering algorithm. Moreover, its validity is illustrated by a real graph data—Books about US politics, not only in cluster validity indices aspect but also in runtime aspect.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115098729","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
Ciphertext Number Full Operations Based on Paillier Algorithm 基于Paillier算法的密文数全运算
2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00124
Gaofeng Yang, Yihong Long
{"title":"Ciphertext Number Full Operations Based on Paillier Algorithm","authors":"Gaofeng Yang, Yihong Long","doi":"10.1109/CIS.2017.00124","DOIUrl":"https://doi.org/10.1109/CIS.2017.00124","url":null,"abstract":"Homomorphic encryption algorithms provide a way for outsourcing computations to the cloud computing while protecting the privacy of the data. Taking into account the fact that there is no practically usable fully homomorphic encrypt-ion algorithm at present, a ciphertext number full operations scheme based on Paillier algorithm is proposed to meet the requirements of operating on the ciphertext numbers in cloud computing. The ciphertext number full operations include the ciphertext number addition, subtraction, multiplication, division and power operations. With this scheme, only a partially homomorphic encryption algorithm is employed to implement the full operations of ciphertext number. The proposed scheme can meet the requirements of performing various forms of computations in the cloud computing. The proposed scheme is a feasible and transitional scheme for cloud computing under the current circumstance that there is no practically usable fully homomorphic encryption algorithm.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130334149","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
An alpha-Dominance Expandation Based Algorithm for Many-Objective Optimization 基于α优势展开的多目标优化算法
2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00010
Junhua Liu, Yuping Wang, Xingyin Wang, Xin Sui, Sixin Guo, Liwen Liu
{"title":"An alpha-Dominance Expandation Based Algorithm for Many-Objective Optimization","authors":"Junhua Liu, Yuping Wang, Xingyin Wang, Xin Sui, Sixin Guo, Liwen Liu","doi":"10.1109/CIS.2017.00010","DOIUrl":"https://doi.org/10.1109/CIS.2017.00010","url":null,"abstract":"The convergence ability of Pareto-based evolutionary algorithms sharply reduces for many-objective optimization problems because the Pareto dominance is inefficient to rank the solutions and result in too many solutions becoming the non-dominated solutions. To overcome this shortcoming, it is necessary to increase the selection pressure toward the global optimal solutions and well-maintain the diversity of obtained non-dominated solutions. In this paper, an improved α-dominance based on expanding the dominated area of α-dominance is proposed. By redefining each objective function and the optimization problem through non-linear transformations, the dominated area of each solution is further expanded compared to that expanded by α-dominance, which can further enhance the selection pressure. Besides, the new dominance can well maintain the diversity of obtained solutions since the dominated area flexibly changes with different solutions. Moreover, this new dominance can be integrated into any multi-objective evolutionary algorithm to improve the performance of this algorithm. To demonstrate the effectiveness of the new dominance, we conduct the experiments on algorithm NNIA combined by the new dominance (briefly NNIA-NLAD). The experimental results show that the improved α-dominance can help NNIA to find better Pareto Front and maintain the diversity of obtained solutions for many-objective optimization problems.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"427 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122516865","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: 4
Modified Binary Cuckoo Search for Feature Selection: A Hybrid Filter-Wrapper Approach 特征选择的改进二进制布谷鸟搜索:一种混合滤波-包装方法
2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00113
Yun Jiang, Xi Liu, Guolei Yan, Jize Xiao
{"title":"Modified Binary Cuckoo Search for Feature Selection: A Hybrid Filter-Wrapper Approach","authors":"Yun Jiang, Xi Liu, Guolei Yan, Jize Xiao","doi":"10.1109/CIS.2017.00113","DOIUrl":"https://doi.org/10.1109/CIS.2017.00113","url":null,"abstract":"Feature selection is an important pre-processing step in classification problems. It can reduce the dimensionality of a dataset and increase the accuracy and efficiency of a learning/classification algorithm. Filter methods are necessary to obtain only the relevant features to the class and to avoid redundancy. While wrapper methods are applied to get optimized features and better classification accuracy. This paper proposes a feature selection based on hybridization of mutual information feature selection (MIFS) filter and modified binary cuckoo search (MBCS) wrapper methods. The classifier accuracy of K-nearest neighbor (KNN) is used as the fitness function. The experimental results show that the hybrid filter-wrapper algorithm maintains the high classification performance achieved by wrapper methods and significantly reduce the computational time. At the same time, it reduces the number of features.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123355422","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: 21
An Effective Solution to Nonlinear Bilevel Programming Problems Using Improved Particle Swarm Optimization Algorithm 基于改进粒子群算法的非线性双层规划问题的有效求解
2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00012
Zhonghua Li, Liping Jia, Caiming Liu
{"title":"An Effective Solution to Nonlinear Bilevel Programming Problems Using Improved Particle Swarm Optimization Algorithm","authors":"Zhonghua Li, Liping Jia, Caiming Liu","doi":"10.1109/CIS.2017.00012","DOIUrl":"https://doi.org/10.1109/CIS.2017.00012","url":null,"abstract":"Nonlinear bilevel programming has a hierarchical structure and has been proved to be NP-hard. In this paper, a class of nonlinear bilevel programming problems is studied. The follower's problem is converted into a series of constraints for the leader's problem by using KKT optimality conditions. For the resultant problem, a method called Chen-Harker-Kanzow-Smalen smoothing method is applied to solve this kind of problem. Later, an improved particle swarm optimization algorithm is designed, and two numerical examples are used to validate the algorithm, and the results show that the algorithm is effective.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134115704","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
Unmanned Aerial Vehicles Rapid Delivery Routing of the Emergency Rescue in the Complex Mountain Region 复杂山区应急救援无人机快速投送路径研究
2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00081
Zhihua Song, Han Zhang, Y. Wang, Linghui Zhang
{"title":"Unmanned Aerial Vehicles Rapid Delivery Routing of the Emergency Rescue in the Complex Mountain Region","authors":"Zhihua Song, Han Zhang, Y. Wang, Linghui Zhang","doi":"10.1109/CIS.2017.00081","DOIUrl":"https://doi.org/10.1109/CIS.2017.00081","url":null,"abstract":"Unmanned Aerial Vehicles rapid delivery routing of the emergency rescue in the complex mountain region was studied as a multistage decision problem. Firstly, the problem is defined in graph theory and the solving framework is analyzed. Secondly, the dynamic programming model is developed due to its multistage characteristic. Thirdly, the tabu list for the dynamic programming model is introduced so that the optimal routing of UAVs can be computed through solving a minimum cost flow problem. Finally, the effectiveness of the dynamic programming model and algorithm is verified through an example.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134261948","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: 4
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