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2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献

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An improved density peaks method for data clustering 一种改进的密度峰数据聚类方法
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802150
Abdulrahman Lotfi, Seyed Amjad Seyedi, P. Moradi
{"title":"An improved density peaks method for data clustering","authors":"Abdulrahman Lotfi, Seyed Amjad Seyedi, P. Moradi","doi":"10.1109/ICCKE.2016.7802150","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802150","url":null,"abstract":"Clustering is a powerful approach for data analysis and its aim is to group objects based on their similarities. Density peaks clustering is a recently introduced clustering method with the advantages of doesn't need any predefined parameters and neither any iterative process. In this paper, a novel density peaks clustering method called IDPC is proposed. The proposed method consists of two major steps. In the first step, local density concept is used to identify cluster centers. In the second step, a novel label propagation method is proposed to form clusters. The proposed label propagation method also uses the local density concept in its process to propagate the cluster labels around the whole data points. The effectiveness of the proposed method has been assessed on a synthetic datasets and also on some real-world datasets. The obtained results show that the proposed method outperformed the other state-of-the art methods.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124256271","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
Distributed weighted averaging-based robust Cubature Kalman Filter for state estimation of nonlinear systems in wireless sensor networks 基于分布加权平均的鲁棒Cubature Kalman滤波器用于无线传感器网络中非线性系统的状态估计
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802117
B. Safarinejadian, Foroogh Mohammadnia
{"title":"Distributed weighted averaging-based robust Cubature Kalman Filter for state estimation of nonlinear systems in wireless sensor networks","authors":"B. Safarinejadian, Foroogh Mohammadnia","doi":"10.1109/ICCKE.2016.7802117","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802117","url":null,"abstract":"This paper studies the problem of distributed state estimation for nonlinear systems in the presence of uncertainty with the Cubature Kalman Filter (CKF) framework by employing distributed weighted averaging in a wireless sensor network. The communication status among sensors is determined via a connected undirected graph. Firstly, each sensor node uses its own measurements and observations to estimate the states of a system locally and independently. Since the algorithm is implemented in the distributed mode and there is not any fusion center, each sensor node communicates with its neighbors through a distributed weighted averaging algorithm where the optimal weight matrix and the corresponding variance of the optimal information fusion are updated in each implementation step. This proposed algorithm does not need any specific information about the plant uncertainty, since uncertainty estimation is considered in the algorithm. Finally, a numerical example is given and the proposed filtering algorithm is evaluated through simulation of a system for a ballistic target tracking.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121005320","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
Parallelization of a color DCT watermarking algorithm using a CUDA-based approach 基于cuda的并行化彩色DCT水印算法
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802123
A. Mohammadabadi, A. Chalechale
{"title":"Parallelization of a color DCT watermarking algorithm using a CUDA-based approach","authors":"A. Mohammadabadi, A. Chalechale","doi":"10.1109/ICCKE.2016.7802123","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802123","url":null,"abstract":"Image watermarking in DCT domain has a high computational complexity especially for color and high resolution images, where usage of them has been significantly grown. To address this issue, in this article, a data-parallel color DCT watermarking approach is proposed and implemented on GPU using CUDA. Also, in this work, before embedding, the color watermark is compressed using a modified method to get less distortion. CUDA implementation of 8×8 DCT offers 12×-43× speedup with GT 540M and 94×-105× speedup with GTX 580, for different image sizes. In case of embedding procedure, the speedup obtained by GT 540M is between 7× and 26×, and the speedup obtained by GTX 580 is between 46× and 73×, for various case studies. Furthermore, in case of extracting procedure, GT 540M leads to a speedup between 10× and 29×, and GTX 580 leads to a speedup between 75× and 80×, for various case studies.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133207372","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: 7
MetaSPD: Metamorphic analysis for automatic Software Piracy Detection MetaSPD:用于软件盗版自动检测的变形分析
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802127
A. Khalilian, Hadi Golbaghi, Amir Nourazar, H. Haghighi, M. Vahidi-Asl
{"title":"MetaSPD: Metamorphic analysis for automatic Software Piracy Detection","authors":"A. Khalilian, Hadi Golbaghi, Amir Nourazar, H. Haghighi, M. Vahidi-Asl","doi":"10.1109/ICCKE.2016.7802127","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802127","url":null,"abstract":"This paper presents a novel approach to address the problem of automatic detection of the software that is pirated. Software piracy, which is the unauthorized reconstruction and distribution of licensed and copyrighted software, imposes high economical and commercial losses annually and is also considered as a drastic threat to the security of software systems. To overcome these issues, we mine the opcode graphs of the software to extract the frequent opcode sub-graphs of the software. In fact, these sub-graphs would act as micro-signatures of the original software and can be used to reason about the pirated versions of the software. Pirated software detection shares many characteristics of metamorphic malware detection where the structure of the code is morphed while the main functionality of the malware is preserved. We evaluated our approach using the same data and experimental setup as a state-of-the-art approach. In our experiments, the proposed approach outperformed the mentioned approach in terms of robustness and effectiveness.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122416364","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
A combined SVM and Markov model approach for splice site identification 一种结合支持向量机和马尔可夫模型的剪接位置识别方法
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802140
Elham Pashaei, Alper Yilmaz, N. Aydin
{"title":"A combined SVM and Markov model approach for splice site identification","authors":"Elham Pashaei, Alper Yilmaz, N. Aydin","doi":"10.1109/ICCKE.2016.7802140","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802140","url":null,"abstract":"Due to an exponential increase in biological sequence data, gene detection has become one of the challenging tasks in computational biology. Splice site prediction is an essential part of the gene detection. Thus, it has great significance to develop efficient methods for accurately identifying splice sites. This paper introduces a novel algorithm to predict the splice sites based on support vector machine (SVM) and a new type of Markov chain model, namely DMM2. The proposed method shows great improvement over most of the current state of art methods, including MM1-SVM, Reduced MM1-SVM, SVM-B, LVMM, MM1-RF, MM2F-SVM, MCM-SVM, DM-SVM and DM2-AdaBoost. The repeated 10-fold cross validation was used to assess the performance of the method on the HS3D dataset. In addition, we applied it to NN269 dataset to examine the stability of the proposed method. The experimental results indicate that the new approach is feasible and efficient.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134342937","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: 10
Towards an spiking deep belief network for face recognition application 面向人脸识别应用的峰值深度信念网络
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802132
Mazdak Fatahi, M. Ahmadi, A. Ahmadi, Mahyar Shahsavari, P. Devienne
{"title":"Towards an spiking deep belief network for face recognition application","authors":"Mazdak Fatahi, M. Ahmadi, A. Ahmadi, Mahyar Shahsavari, P. Devienne","doi":"10.1109/ICCKE.2016.7802132","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802132","url":null,"abstract":"Understanding brain mechanisms and its problem solving techniques is the motivation of many emerging brain inspired computation methods. In this paper, respecting deep architecture of the brain and spiking model of biological neural networks, we propose a spiking deep belief network to evaluate ability of the deep spiking neural networks in face recognition application on ORL dataset. To overcome the change of using spiking neural networks in a deep learning algorithm, Siegert model is utilized as an abstract neuron model. Although there are state of the art classic machine learning algorithms for face detection, this work is mainly focused on demonstrating capabilities of brain inspired models in this era, which can be serious candidate for future hardware oriented deep learning implementations. Accordingly, the proposed model, because of using leaky integrate-and-fire neuron model, is compatible to be used in efficient neuromorphic platforms for accelerators and hardware implementation.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133984343","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: 15
A survey on participant recruitment in crowdsensing systems 众感系统中参与者招募的调查
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802154
Milad Davari, H. Amintoosi
{"title":"A survey on participant recruitment in crowdsensing systems","authors":"Milad Davari, H. Amintoosi","doi":"10.1109/ICCKE.2016.7802154","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802154","url":null,"abstract":"Advances in the sensing capabilities of smartphones have resulted in the emergence of crowdsensing. In a crowdsensing campaign, ordinary citizens are recruited to collect sensor data from nearby environments which are then analysed to provide useful information. In order for a crowdsensing application to be a success, sufficient number of well-suited participants should be recruited to contribute. In this paper, we make a review on the works presented to address the challenge of participant recruitment in crowdsensing systems. We first have a short review on the related works in the area of online communities and crowdsourcing systems. Then, we present a review of the works specifically related to crowdsensing systems.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123689651","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: 14
A centralized reinforcement learning method for multi-agent job scheduling in Grid 网格中多智能体作业调度的集中强化学习方法
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-09-11 DOI: 10.1109/ICCKE.2016.7802135
M. Moradi
{"title":"A centralized reinforcement learning method for multi-agent job scheduling in Grid","authors":"M. Moradi","doi":"10.1109/ICCKE.2016.7802135","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802135","url":null,"abstract":"One of the main challenges in Grid systems is designing an adaptive, scalable, and model-independent method for job scheduling to achieve a desirable degree of load balancing and system efficiency. Centralized job scheduling methods have some drawbacks, such as single point of failure and lack of scalability. Moreover, decentralized methods require a coordination mechanism with limited communications. In this paper, we propose a multi-agent approach to job scheduling in Grid, named Centralized Learning Distributed Scheduling (CLDS), by utilizing the reinforcement learning framework. The CLDS is a model free approach that uses the information of jobs and their completion time to estimate the efficiency of resources. In this method, there are a learner agent and several scheduler agents that perform the task of learning and job scheduling with the use of a coordination strategy that maintains the communication cost at a limited level. We evaluated the efficiency of the CLDS method by designing and performing a set of experiments on a simulated Grid system under different system scales and loads. The results show that the CLDS can effectively balance the load of system even in large scale and heavy loaded Grids, while maintains its adaptive performance and scalability.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132207274","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
Training LDCRF model on unsegmented sequences using connectionist temporal classification 使用连接时间分类在未分割序列上训练LDCRF模型
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-06-26 DOI: 10.1109/ICCKE.2016.7802153
A. A. Atashin, Kamaledin Ghiasi-Shirazi, A. Harati
{"title":"Training LDCRF model on unsegmented sequences using connectionist temporal classification","authors":"A. A. Atashin, Kamaledin Ghiasi-Shirazi, A. Harati","doi":"10.1109/ICCKE.2016.7802153","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802153","url":null,"abstract":"Many machine learning problems such as speech recognition, gesture recognition, and handwriting recognition are concerned with simultaneous segmentation and labeling of sequence data. Latent-dynamic conditional random field (LDCRF) is a well-known discriminative method that has been successfully used for this task. However, LDCRF can only be trained with pre-segmented data sequences in which the label of each frame is available apriori. In the realm of neural networks, the invention of connectionist temporal classification (CTC) made it possible to train recurrent neural networks on unsegmented sequences with great success. In this paper, we use CTC to train an LDCRF model on unsegmented sequences. Experimental results on two gesture recognition tasks show that the proposed method outperforms LDCRFs, hidden Markov models, and conditional random fields.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130151045","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
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