Pub Date : 2020-11-01DOI: 10.1109/CIS52066.2020.00020
Mengmeng Xu, Hai Zhu, Hengzhou Xu, Jiongjiong Song, Zhen Luo
In this paper, the reliable routing design problem is investigated in predictable wireless networks over unreliable links. The predictable wireless networks are described as a sequence of static graphs, and then modeled as a space-time graph. The reliable routing design problem on the space-time graph is defined as a bi-objective optimization problem. The aim of the new routing design problem is to find a routing path with the maximum routing reliability and the minimum routing cost. Next, a hierarchical shortest routing algorithm is proposed to find the feasible routing path. Simulation results validate the effectiveness of the proposed routing algorithm.
{"title":"Reliable Routing Design in Predictable Wireless Networks with Unreliable Links","authors":"Mengmeng Xu, Hai Zhu, Hengzhou Xu, Jiongjiong Song, Zhen Luo","doi":"10.1109/CIS52066.2020.00020","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00020","url":null,"abstract":"In this paper, the reliable routing design problem is investigated in predictable wireless networks over unreliable links. The predictable wireless networks are described as a sequence of static graphs, and then modeled as a space-time graph. The reliable routing design problem on the space-time graph is defined as a bi-objective optimization problem. The aim of the new routing design problem is to find a routing path with the maximum routing reliability and the minimum routing cost. Next, a hierarchical shortest routing algorithm is proposed to find the feasible routing path. Simulation results validate the effectiveness of the proposed routing algorithm.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133994392","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}
Pub Date : 2020-11-01DOI: 10.1109/CIS52066.2020.00043
Hai-Dong Li, Lan Zhang
Under the background of educational reform in the new era, the transformation and development of Guangdong independent colleges is urgent and necessary. The connotation of the transformation and development of independent colleges includes two aspects: the internal transformation of connotation promotion and the external transformation of school running organizational form. The transformation and development of Guangdong independent college has strengths and weaknesses, and it is faced with opportunities and threats. According to AHP weight analysis, it is concluded that the first choice of transformation of Guangdong independent college is ST strategy which is to give full play to its strengths and overcome the threat.
{"title":"Research on the Transformation and Development Strategy of Guangdong Independent College Based on SWOT-AHP","authors":"Hai-Dong Li, Lan Zhang","doi":"10.1109/CIS52066.2020.00043","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00043","url":null,"abstract":"Under the background of educational reform in the new era, the transformation and development of Guangdong independent colleges is urgent and necessary. The connotation of the transformation and development of independent colleges includes two aspects: the internal transformation of connotation promotion and the external transformation of school running organizational form. The transformation and development of Guangdong independent college has strengths and weaknesses, and it is faced with opportunities and threats. According to AHP weight analysis, it is concluded that the first choice of transformation of Guangdong independent college is ST strategy which is to give full play to its strengths and overcome the threat.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125229938","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}
Pub Date : 2020-11-01DOI: 10.1109/CIS52066.2020.00072
Caiming Liu, Yan Zhang, Qin Li, Luxin Xiao
The immune mechanism plays an unique role in improving the performance of network intrusion detection. However, the traditional immune method fails to give full play to the detection performance of the immune mechanism. In order to solve the above problems, this paper uses KDD CUP'99 as the detection object, and a network anomaly detection method with full coverage of immune detectors is proposed. Based on the immune principle, the intrusion detection process for the data set to be detected is constructed, the expression method of network connection is defined, the immune element data set under the intrusion detection environment are simulated, the classification detection mechanism of memory detector is defined, and the full coverage detection of the detected antigen is realized. A network connection similarity computing method based on the characteristics of the data set to be detected is proposed. The experimental scheme was constructed and the experiment was carried out. The experimental results show that the detection method proposed in this paper can detect all antigens with full coverage and has high performance of intrusion detection.
{"title":"Full Coverage Detection of Immune Detector for Public Data Set","authors":"Caiming Liu, Yan Zhang, Qin Li, Luxin Xiao","doi":"10.1109/CIS52066.2020.00072","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00072","url":null,"abstract":"The immune mechanism plays an unique role in improving the performance of network intrusion detection. However, the traditional immune method fails to give full play to the detection performance of the immune mechanism. In order to solve the above problems, this paper uses KDD CUP'99 as the detection object, and a network anomaly detection method with full coverage of immune detectors is proposed. Based on the immune principle, the intrusion detection process for the data set to be detected is constructed, the expression method of network connection is defined, the immune element data set under the intrusion detection environment are simulated, the classification detection mechanism of memory detector is defined, and the full coverage detection of the detected antigen is realized. A network connection similarity computing method based on the characteristics of the data set to be detected is proposed. The experimental scheme was constructed and the experiment was carried out. The experimental results show that the detection method proposed in this paper can detect all antigens with full coverage and has high performance of intrusion detection.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127805362","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}
Pub Date : 2020-11-01DOI: 10.1109/CIS52066.2020.00010
Runjia Wu, Fangqing Gu, Jie Huang
Deep Deterministic Policy Gradient is a reinforcement learning method, which is widely used in unmanned aerial vehicle (UAV) for path planning. In order to solve the environmental sensitivity in path planning, we present an improved deep deterministic policy gradient for UAV path planning. Simulation results demonstrate that the algorithm improves the convergence speed, convergence effect and stability. The UAV can learn more knowledge from the complex environment.
{"title":"A multi-critic deep deterministic policy gradient UAV path planning","authors":"Runjia Wu, Fangqing Gu, Jie Huang","doi":"10.1109/CIS52066.2020.00010","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00010","url":null,"abstract":"Deep Deterministic Policy Gradient is a reinforcement learning method, which is widely used in unmanned aerial vehicle (UAV) for path planning. In order to solve the environmental sensitivity in path planning, we present an improved deep deterministic policy gradient for UAV path planning. Simulation results demonstrate that the algorithm improves the convergence speed, convergence effect and stability. The UAV can learn more knowledge from the complex environment.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130595235","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}
Pub Date : 2020-11-01DOI: 10.1109/CIS52066.2020.00055
Zhihe Wang, Shuyan Wang, Hui Du, Hao Guo
Traditional fuzzy C-means (FCM) clustering algorithm is sensitive to initial clustering center, and the number of clusters need to be set artificially in advance. For these reasons, we propose an improved FCM algorithm (AMMF) that can determine the number of clusters automatically. Firstly, the proposed algorithm uses the affinity propagation clustering algorithm to obtain coarse number of clusters, which are taken as the upper limit of searching the best number of clusters. Secondly, by the improved maximum and minimum distance algorithm obtains some representative sample points as the initial clustering centers of the FCM algorithm. Lastly, we use Silhouette Coefficient to analyze the quality of clustering to determine the optimal number of clusters automatically. Experimental results show that the AMMF algorithm has significantly better clustering performance than other improved FCM based algorithms, and improves the stability of the clustering results.
{"title":"Fuzzy C-means clustering algorithm for automatically determining the number of clusters","authors":"Zhihe Wang, Shuyan Wang, Hui Du, Hao Guo","doi":"10.1109/CIS52066.2020.00055","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00055","url":null,"abstract":"Traditional fuzzy C-means (FCM) clustering algorithm is sensitive to initial clustering center, and the number of clusters need to be set artificially in advance. For these reasons, we propose an improved FCM algorithm (AMMF) that can determine the number of clusters automatically. Firstly, the proposed algorithm uses the affinity propagation clustering algorithm to obtain coarse number of clusters, which are taken as the upper limit of searching the best number of clusters. Secondly, by the improved maximum and minimum distance algorithm obtains some representative sample points as the initial clustering centers of the FCM algorithm. Lastly, we use Silhouette Coefficient to analyze the quality of clustering to determine the optimal number of clusters automatically. Experimental results show that the AMMF algorithm has significantly better clustering performance than other improved FCM based algorithms, and improves the stability of the clustering results.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123380467","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}
Pub Date : 2020-11-01DOI: 10.1109/CIS52066.2020.00079
G. Hu, Chuhan Zhou, Xiaojie Zhang, Han Zhang, Zhihua Song, Zhongliang Zhou
Combat units in joint operations have huge decision space and many uncertain factors. Generally speaking, most of the traditional decision-making methods are based on rules, and it is impossible to establish a reliable mapping relationship between decision space and combat results. To promote the research of intelligent decision making in joint operations, the Equipment Development Department of the Central Military Commission held a challenge called ‘strategic plans on the computer, joint intelligent win’. In this challenge, the forces and the performance equipment are fixed at both the sides of the attack and defense. This setup helps the intelligent deci-sion-making agents to identify the scenarios which score high and have good learning scope in decision making. In the study, we propose an air offensive operations decision-making agent based on a neural network. To perform testing and analysis, we have used the neural network dataset available at a decision space. The decision space comprises of different decision-making rules and rando disturbances. The proposed model shows better results as compared to traditional rule-based operations and military expert decision-based operations in the test set.
{"title":"A Neural Network-Based Intelligent Decision-Making in the Air-Offensive Campaign with Simulation","authors":"G. Hu, Chuhan Zhou, Xiaojie Zhang, Han Zhang, Zhihua Song, Zhongliang Zhou","doi":"10.1109/CIS52066.2020.00079","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00079","url":null,"abstract":"Combat units in joint operations have huge decision space and many uncertain factors. Generally speaking, most of the traditional decision-making methods are based on rules, and it is impossible to establish a reliable mapping relationship between decision space and combat results. To promote the research of intelligent decision making in joint operations, the Equipment Development Department of the Central Military Commission held a challenge called ‘strategic plans on the computer, joint intelligent win’. In this challenge, the forces and the performance equipment are fixed at both the sides of the attack and defense. This setup helps the intelligent deci-sion-making agents to identify the scenarios which score high and have good learning scope in decision making. In the study, we propose an air offensive operations decision-making agent based on a neural network. To perform testing and analysis, we have used the neural network dataset available at a decision space. The decision space comprises of different decision-making rules and rando disturbances. The proposed model shows better results as compared to traditional rule-based operations and military expert decision-based operations in the test set.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128192694","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}
Pub Date : 2020-11-01DOI: 10.1109/CIS52066.2020.00013
Liefa Liao, Zhiming Li
Deep hashing algorithm is one of the most effective techniques for the approximate nearest neighbor search for large-scale image retrieval. Existing deep hash algorithms are based on paired labels and triple ordering loss, they usually only interact with one negative class, and the convergence speed is too slow. In this paper, we propose a novel deep hashing algorithm called N-pair loss deep hashing (NPLDH), which optimization based on the N-pair loss function can help deep hash models to train more effectively. Experimental results show that our NPLDH algorithm achieves higher performance in image retrieval algorithms on the CIFAR-10 and NUS-WIDE datasets.
{"title":"Deep Hashing Using N-pair Loss for Image Retrieval","authors":"Liefa Liao, Zhiming Li","doi":"10.1109/CIS52066.2020.00013","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00013","url":null,"abstract":"Deep hashing algorithm is one of the most effective techniques for the approximate nearest neighbor search for large-scale image retrieval. Existing deep hash algorithms are based on paired labels and triple ordering loss, they usually only interact with one negative class, and the convergence speed is too slow. In this paper, we propose a novel deep hashing algorithm called N-pair loss deep hashing (NPLDH), which optimization based on the N-pair loss function can help deep hash models to train more effectively. Experimental results show that our NPLDH algorithm achieves higher performance in image retrieval algorithms on the CIFAR-10 and NUS-WIDE datasets.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125865978","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}
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