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2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献

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Broad Learning System for Class Incremental Learning 课堂增量式学习的广义学习系统
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965551
Ruizhi Han, C. L. P. Chen, Shuang Feng
{"title":"Broad Learning System for Class Incremental Learning","authors":"Ruizhi Han, C. L. P. Chen, Shuang Feng","doi":"10.1109/SPAC46244.2018.8965551","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965551","url":null,"abstract":"The large-scale image datasets such as ImageNet and open-ended photo websites are revealing new challenges to image classification that were not apparent in smaller and fixed sets. In particular, how to handle the dynamically growing datasets efficiently, where not only the amount of training data but also the number of classes increases over time, remains an unexplored problem. In this challenging setting, we study how to employ the Broad Learning System (BLS) to deal with the incremental increases of sample classes in datasets. We first determine whether a new batch of samples is from certain seen classes or unseen classes by two methods based on the statistics theory. Then, the incremental training algorithms are developed to classify unlearned data from new classes. We test our class incremental learning algorithms of BLS on some representative datasets such as Cifar-100. The experimental results show that our methods can accurately distinguish seen and unseen classes. And the comparison study demonstrates that our method can be extended to 100 classes on Cifar-100 with an acceptable loss of accuracy and performs better compared to other similar class incremental algorithms such as iCaRL.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114222025","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
Wavelet Broad Learning Filter: A Novel Adaptive Filter for estimating the Physiological Tremor in Teleoperation 小波广义学习滤波器:一种用于远程操作中生理震动估计的自适应滤波器
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965489
Jiatai Lin, Zhi Liu, Jin Lai
{"title":"Wavelet Broad Learning Filter: A Novel Adaptive Filter for estimating the Physiological Tremor in Teleoperation","authors":"Jiatai Lin, Zhi Liu, Jin Lai","doi":"10.1109/SPAC46244.2018.8965489","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965489","url":null,"abstract":"In this paper, a wavelet broad learning filter is proposed to estimate the tremor. At first, the structure of original broad learning system (BLS) is redesigned. To extract the features of tremor, the novel WBLAF maps each dimensional data as the feature nodes respectively by the wavelet function. Secondly, a novel self-paced wavelet auto-encoder (SPWAE) is proposed to train the weights of feature mapping. In addition, the ridge regression learning algorithm and the incremental learning of the proposed filter are applied to learning online. Finally, semiphysical simulation experiment is accomplished. As shown in the results, the new proposed WBLAF can effectively estimate and filter out the physiological tremor in tele-operation.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124055647","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
Synchronization of Delay Memristive System with Piecewise Function based on a Disturbance Observer 基于扰动观测器的分段函数延迟记忆系统同步
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965483
Li Li, Pengcheng Wei
{"title":"Synchronization of Delay Memristive System with Piecewise Function based on a Disturbance Observer","authors":"Li Li, Pengcheng Wei","doi":"10.1109/SPAC46244.2018.8965483","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965483","url":null,"abstract":"This paper is concerned with synchronization of four-dimensional delay memristive system with piecewise function based on disturbance observers via adaptive controllers. In the consideration of disturbances in the response system, novel disturbance observers are investigated to estimate and compensate the disturbances. Meanwhile, by developing adaptive controllers, synchronization between the special drive and the corresponding response system is achieved. It is worth mentioning that this approach can be safely conducted to other systems with or without delays.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122175084","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 improved fuzzy c-means clustering algorithm with guided filter for Image Segmentation 一种改进的带引导滤波的模糊c均值聚类算法用于图像分割
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965448
Guangmei Xu, Jiwen Dong, Jin Zhou, Yingxu Wang, Bozhan Dang, Dong Wang, Lin Wang, Shiyuan Han
{"title":"An improved fuzzy c-means clustering algorithm with guided filter for Image Segmentation","authors":"Guangmei Xu, Jiwen Dong, Jin Zhou, Yingxu Wang, Bozhan Dang, Dong Wang, Lin Wang, Shiyuan Han","doi":"10.1109/SPAC46244.2018.8965448","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965448","url":null,"abstract":"Fuzzy c-means clustering with guided filter (FCM+GF) is an effective method for noisy image segmentation. However, the parameter ε of guided filter in the FCM+GF is set to a fixed value, which weakens the ability of the FCM+GF to partition images with different noise rates. In this paper, an improved fuzzy c-means with guided filter method (FCM+GF_I) is proposed. In our method, a new influence factor ρ is defined to adjust the guidance image. By adjusting the value of ρ, the proposed FCM+GF_I method achieves good performance on different noisy images. Experiments on Brain MR images show the superiority and efficiency of our method.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117021497","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
SEDalvik: A Kernel-Level Android Behavior Forensic Method SEDalvik:一种内核级Android行为取证方法
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965577
Fujia Cheng, Chengxiang Tan
{"title":"SEDalvik: A Kernel-Level Android Behavior Forensic Method","authors":"Fujia Cheng, Chengxiang Tan","doi":"10.1109/SPAC46244.2018.8965577","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965577","url":null,"abstract":"Android is the mobile operating system with the highest market share, but it comes with the endless malicious code. Behavior forensics has an extremely important role in ensuring application security. However, most of the existing methods of forensic analysis work at the application layer, not universal and easily evaded by anti-forensics mechanisms. Therefore, we propose a behavior forensics method based on source code of Dalvik virtual machine and work at the kernel layer, which effectively improves the versatility and effectiveness of behavior forensics on Android.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124728256","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
Fabric Defect Detection Method Based on Sparse and Dense Mixed Low-rank Decomposition 基于稀疏与密集混合低秩分解的织物缺陷检测方法
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965570
Yan Yang, Junpu Wang, Zhoufeng Liu, Chunlei Li, Bicao Li, Qingwei Xu
{"title":"Fabric Defect Detection Method Based on Sparse and Dense Mixed Low-rank Decomposition","authors":"Yan Yang, Junpu Wang, Zhoufeng Liu, Chunlei Li, Bicao Li, Qingwei Xu","doi":"10.1109/SPAC46244.2018.8965570","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965570","url":null,"abstract":"On account of the issue that there is severe noise in the detection of defects by the traditional low-rank decomposition defect detection method, in this paper, we present an efficient fabric defect detection approach which utilizes the sparse and dense decomposition on the base of low-rank representation. Firstly, the fabric image is uniformly segmented into image blocks. Each image block is spanned into a column vector, which is assembled to constitute the fabric image feature matrix. Then, the sparse and dense mixed low-rank decomposition model is constructed with the introduction of the F norm. The presented model is optimized by alternating direction multiplier method (ADMM) and augmented Lagrange multiplier (ALM), and the low rank array, dense matrix and sparse array are obtained. Finally, a thresholding segmentation approach is employed to detect the defect area by partitioning the salience map. Experimental results demonstrate that the proposed method achieves an efficient detection property, and it is superior to the current approaches.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129647604","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
Hyper-parameter Analysis of the Improved Deep Embedding Clustering Model 改进的深度嵌入聚类模型的超参数分析
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965509
Qiying Feng, C. L. P. Chen, Jin Zhou
{"title":"Hyper-parameter Analysis of the Improved Deep Embedding Clustering Model","authors":"Qiying Feng, C. L. P. Chen, Jin Zhou","doi":"10.1109/SPAC46244.2018.8965509","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965509","url":null,"abstract":"Clustering has gained large successful in many areas in machine learning especially the computer vision and knowledge discovery. However, the clustering tasks on the high-dimension data is still the bottleneck of the clustering. And this problem mainly due to the lack of representative and important features of the data. Recently, the efficient deep clustering models including the deep embedded clustering model (DEC) and the improved deep embedded clustering model (IDEC) are proposed to solve this problem. They validated their models on the real-world datasets and improved the clustering performance compared with the traditional clustering algorithm, which have demonstrated that the superior of DEC and IDEC. Both the clustering method used in DEC and IDEC are student-t distribution, and we choose the IDEC model for further studying the robustness of these two models. Therefore, we discuss how sensitivity of hyper-parameter influence in the IDEC model, and comparative experiments are given to show the sensitivities of the hyper-meters in IDEC in term of the clustering performances.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127561628","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
Haze weather recognition based on multiple features and Random Forest 基于多特征和随机森林的雾霾天气识别
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965544
Xue Fan, Zhiquan Feng, Xiaohui Yang, Tao Xu, Jinglan Tian, Na Lv
{"title":"Haze weather recognition based on multiple features and Random Forest","authors":"Xue Fan, Zhiquan Feng, Xiaohui Yang, Tao Xu, Jinglan Tian, Na Lv","doi":"10.1109/SPAC46244.2018.8965544","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965544","url":null,"abstract":"A single image based haze weather recognition is the fundamental operation of the applications of outdoor computer vision. Currently, the recognition results are remains undesirable and most existing methods are only for the fixed scene. In this paper, we propose multiple features and Random Forest based haze weather classification method for any scenario to improve the detection accuracy. First, through systematically investigation, multiple features are extracted and properly processed. Then, these features are combined into high dimension vectors and the Random Forest is adopted to lean an adaptive classifier for haze recognition. In the experiment, an outdoor image set which contains around 4000 images is collected. Form the experimental results is can be seen that the proposed method achieves 97.4% recognition accuracy of the haze weather on the collected dataset.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121496040","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
TrafficPSSF: A Fast and An Effective Malware Detection Under Online and Offline TrafficPSSF:一种在线和离线下快速有效的恶意软件检测方法
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965602
Qi He, Zhenxiang Chen, Anli Yan, Lizhi Peng, Chuan Zhao, Yuliang Shi
{"title":"TrafficPSSF: A Fast and An Effective Malware Detection Under Online and Offline","authors":"Qi He, Zhenxiang Chen, Anli Yan, Lizhi Peng, Chuan Zhao, Yuliang Shi","doi":"10.1109/SPAC46244.2018.8965602","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965602","url":null,"abstract":"The use of Android phones is becoming more and more widespread, and Android malware is also entering everyone’s field of vision. In this paper, we propose TrafficPSSF as a fast and an effective method for traffic detection and classification under online and offline detection. The traffic collection platform collects traffic data of application. Especially, we design an online detection and offline detection. One of the features of TCP session is the packet size, which is used for online detection. We can detect malicious traffic without waiting for all traffic packets to arrive, which can improve efficiency. What’s more, we use combination classifier model for our server to increase the accuracy of malicious detection. In the offline detection, we use seven statistical features of TCP as our model input and random forest algorithm for model training. The experiment shows that the online detection has 98.35% of the malicious detection rate, and the offline detection and classification accuracy rate reach 99.98%.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127002679","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
A Fuzzy Adaptive Control Strategy for Active Suspension Systems with Unknown Dynamics 未知动力学主动悬架系统的模糊自适应控制策略
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965615
Hao Sun, Yong-ming Li, Shaocheng Tong
{"title":"A Fuzzy Adaptive Control Strategy for Active Suspension Systems with Unknown Dynamics","authors":"Hao Sun, Yong-ming Li, Shaocheng Tong","doi":"10.1109/SPAC46244.2018.8965615","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965615","url":null,"abstract":"A fuzzy adaptive control method is developed for active suspension systems with unknown dynamics. The controlled object is a single-input multiple-output five-order active suspension system with electromagnetic actuator. Fuzzy logic systems (FLSs) are utilized to identify the unknown nonlinear dynamics. Based on the back-stepping design principle and Lyapunov stability theory, the proposed control scheme is proved to guarantee that the closed-loop system is stable. Simulation results are presented to demonstrate the effectiveness of the developed control strategy.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131619995","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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