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2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献

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On feature prediction in temporal social networks based on artificial neural network learning 基于人工神经网络学习的时态社会网络特征预测
2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167896
Saina Mohamadyari, Niousha Attar, Sadegh Aliakbary
{"title":"On feature prediction in temporal social networks based on artificial neural network learning","authors":"Saina Mohamadyari, Niousha Attar, Sadegh Aliakbary","doi":"10.1109/ICCKE.2017.8167896","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167896","url":null,"abstract":"The study of network features is an important analysis method for the social networks, and prediction of network features is a research problem with many applications, particularly in decision making. In this paper, we propose a novel feature prediction method for temporal social networks, which estimates network measurements in the future based on a small window of measurements in the past. We utilized artificial neural networks as a supervised learning algorithm for training the estimation functions. The comprehensive evaluations show that the proposed method outperforms alternative baselines remarkably according to the prediction accuracy.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114408734","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
Inferring gene regulatory network using path consistency algorithm based on conditional mutual information and genetic algorithm 基于条件互信息和遗传算法的路径一致性算法推断基因调控网络
2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167936
S. Iranmanesh, Vahid Sattari-Naeini, B. Ghavami
{"title":"Inferring gene regulatory network using path consistency algorithm based on conditional mutual information and genetic algorithm","authors":"S. Iranmanesh, Vahid Sattari-Naeini, B. Ghavami","doi":"10.1109/ICCKE.2017.8167936","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167936","url":null,"abstract":"The interactions between genes can be described in the form of an intrinsic and interwoven network called Gene Regulatory Network. Discovering this interaction and accurate modeling of Gene Regulatory Network is one of the key issues in understanding the fundamental cell processes which may be used in various medical, complex genetic diseases and drug discovery applications. In this paper, a method for inferring the gene regulatory network using a combination of Genetic Algorithm and Path Consistency Algorithm based on Conditional Mutual information is presented. In this method, for each gene, a genetic algorithm is utilized to find the most suitable predictor set of that gene. Moreover, in order to reduce the search space, the initial population for each target gene is created using the predictors obtained from Path Consistency Algorithm based on Conditional Mutual information method. To guide Genetic Algorithm, the multiple Pearson correlation coefficient is used. The obtained results using three evaluation criteria for biological data show that the proposed model performs better than recent similar methods.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129041543","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
Word prediction from fMRI data based on C-SVC and a series classifier 基于C-SVC和序列分类器的fMRI数据词预测
2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167941
F. Jalali, A. Ebrahimi, S. Alirezazadeh
{"title":"Word prediction from fMRI data based on C-SVC and a series classifier","authors":"F. Jalali, A. Ebrahimi, S. Alirezazadeh","doi":"10.1109/ICCKE.2017.8167941","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167941","url":null,"abstract":"Word prediction is an applicable task for medical purposes and it can be done by analyzing brain's activities. Functional Magnetic Resonance Imaging (fMRI) is a technique for obtaining 3D images, related to the neural activity of brain through time. By subtracting fMRI images, which are captured consecutively, brain's operation can be detected. In this paper, a novel approach, based on machine learning algorithms, is designed to predict words from fMRI data. In the proposed method, after dimensionality reduction by means of principal component analysis (PCA), C-support vector classification (C-SVC) and a series classifier are applied for fMRI data classification. Results of the proposed method is compared with other classification approaches. Experiments show that the proposed method increases precisian of fMRI data classification and word prediction reliably and incredibly.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128910016","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
Single image camera identification using I-vectors 使用i向量的单图像相机识别
2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167913
Arash Rashidi, F. Razzazi
{"title":"Single image camera identification using I-vectors","authors":"Arash Rashidi, F. Razzazi","doi":"10.1109/ICCKE.2017.8167913","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167913","url":null,"abstract":"Recently, in the field of speech processing, I-Vector modeling has been appealed a great deal of interest. I-Vector has shown its benefits in modeling of intra and inter-domain variabilities to a single low dimension space for speaker identification tasks. This paper presents the usage of I-Vector in camera identification as a new approach in image forensics domain. In our approach, image texture is extracted from images as our features for the I-vector system. We have used 8 camera models in our work and the result shows 99.01% accuracy. We have also conducted attacks on the test images. We gained 99.01% accuracy for rotation attack and the average accuracy of 88.71% for three level brightness attack.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127817535","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
Optimal control of HIV stochastic model through genetic algorithm 基于遗传算法的HIV随机模型最优控制
2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167912
Fatemeh Saeedizadeh, R. Moghaddam
{"title":"Optimal control of HIV stochastic model through genetic algorithm","authors":"Fatemeh Saeedizadeh, R. Moghaddam","doi":"10.1109/ICCKE.2017.8167912","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167912","url":null,"abstract":"This paper presents an optimal control of a HIV stochastic model through drug therapy. The model shows the effect of anti-retrovirus drugs in different stages of infection. The optimal controller is achieved by Genetic Algorithm (GA). In this paper we find appropriate efficacies of a drug that minimize the virus particles for a deterministic model and stochastic model. To design the optimal stochastic controller, the stochastic model is converted to a deterministic model. Genetic Algorithm provides discrete constraints. A nonlinear constraint changed into two linear constraints by discretization. In the first part of simulation, the behavior of the system with constant value of efficacy is shown. Finally, for the objective of this problem different values of efficacy are found, which leads to the best drug dosage. The results demonstrate that optimal control by ignoring statistical properties, will not be efficient.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129807548","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
SDT-free: An efficient crosstalk avoidance coding mechanism considering inductance effects SDT-free:一种考虑电感效应的高效串扰避免编码机制
2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167894
Z. Shirmohammadi, S. Miremadi
{"title":"SDT-free: An efficient crosstalk avoidance coding mechanism considering inductance effects","authors":"Z. Shirmohammadi, S. Miremadi","doi":"10.1109/ICCKE.2017.8167894","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167894","url":null,"abstract":"An efficient numeral-based coding mechanism, called SDT-free is proposed in this paper that avoids crosstalk faults. The SDT-free coding mechanism completely removes bit patterns ‘11111’ and ‘00000’ which impose the worst crosstalk effects considering inductance effects. In this way, the coding mechanism improves the reliability of chip channels and offers invariant delay for channels. To minimize overheads of SDT-free coding mechanism, a novel numeral system is used in generating code words. Using this numeral system, the coding mechanism is applicable for desired numbers of wires. The SDT-free coding mechanism has been evaluated by means of VHDL simulations in terms of area occupation, power consumption and critical path of codec. Evaluations confirm that the SDT-free coding mechanism completely avoids worst crosstalk-induced transition patterns in NoC channels. In addition, the SDT-free has been compared with the Fibonacci-based coding mechanism. SDT-free coding mechanism beside considering inductance effects provides improvements of 8.8% in power consumption and 20.1% in area occupation as compared to Fibonacci-based coding mechanism.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126035288","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
Bilingualism advantage in handwritten character recognition: A deep learning investigation on Persian and Latin scripts 手写体字符识别的双语优势:波斯和拉丁文字的深度学习研究
2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167923
Zahra Sadeghi, Alberto Testolin, M. Zorzi
{"title":"Bilingualism advantage in handwritten character recognition: A deep learning investigation on Persian and Latin scripts","authors":"Zahra Sadeghi, Alberto Testolin, M. Zorzi","doi":"10.1109/ICCKE.2017.8167923","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167923","url":null,"abstract":"In this study, we investigated the effects of mastering multiple scripts in handwritten character recognition by means of computational simulations. In particular, we trained a set of deep neural networks on two different datasets of handwritten characters: the HODA dataset, which is a collection of images of handwritten Persian digits, and the MNIST dataset, which contains Latin handwritten digits. We simulated native language individuals (trained on a single dataset) as well as bilingual individuals (trained on both datasets), and compared their performance in a recognition task performed under different noisy conditions. Our results show the superior performance of bilingual networks in handwritten digit recognition in comparison to the monolingual networks, thereby suggesting that mastering multiple languages might facilitate knowledge transfer across similar domains.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115778323","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
An analytical model for segmented bus enhanced network on chip 片上分段总线增强网络的分析模型
2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167932
Hossein Bastan, M. A. Montazeri, Amin Ghalami Osgouei, A. Khorsandi, H. Saidi
{"title":"An analytical model for segmented bus enhanced network on chip","authors":"Hossein Bastan, M. A. Montazeri, Amin Ghalami Osgouei, A. Khorsandi, H. Saidi","doi":"10.1109/ICCKE.2017.8167932","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167932","url":null,"abstract":"Several analytical models have been proposed for wormhole-routing network on chip while there is few analytical model for fully adaptive routing. To the best of our knowledge, there is not analytical model for segmented bus. In this paper, a new analytical delay model presented for hybrid Bus-NoC. First we present an analytical model for segmented bus, then by improving the mesh analytical model, we try to combine mesh and segmented bus. And finally, by applying some simulation results for hybrid Bus-NoC architecture we show that the hybrid model achieve a good degree of accuracy under different working conditions.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133881674","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
Contract verification of ETL transformations ETL转换的契约验证
2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167945
Banafsheh Azizi, B. Zamani, S. Kolahdouz-Rahimi
{"title":"Contract verification of ETL transformations","authors":"Banafsheh Azizi, B. Zamani, S. Kolahdouz-Rahimi","doi":"10.1109/ICCKE.2017.8167945","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167945","url":null,"abstract":"Model driven engineering is a new paradigm in software engineering in which software is automatically generated from the model via applying transformations. Model transformations, which are defined using transformation languages, play the major role in model driven approaches. During the last decade, different transformation languages have been introduced to the model driven community. Epsilon Transformation Language (ETL) is one of the most widely used ones across the community. Since the correctness of a transformation has direct impact on generating the final product, verification of a model transformation is an important issue. In this paper, we aim to propose an approach to verify the correctness of ETL transformations. Our proposal is to use DSLTrans, which is a graph transformation language, as well as the SyVOLT tool, which provides symbolic execution of DSLTrans transformations. To achieve our goal, first we transform the ETL transformation to DSLTrans, then, using the SyVOLT tool, we verify the transformation. To evaluate our approach, a case study is performed and the results suggest its capability to detect errors that previously were not easily identifiable.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114594345","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: 5
VLAG: A very fast locality approximation model for GPU kernels with regular access patterns vladg:一个非常快速的局部性近似模型，用于有规则访问模式的GPU内核
2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167887
Mohsen Kiani, Amir Rajabzadeh
{"title":"VLAG: A very fast locality approximation model for GPU kernels with regular access patterns","authors":"Mohsen Kiani, Amir Rajabzadeh","doi":"10.1109/ICCKE.2017.8167887","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167887","url":null,"abstract":"Performance modeling plays an important role for optimal hardware design and optimized application implementation. This paper presents a very low overhead performance model, called VLAG, to approximate the data localities exploited by GPU kernels. VLAG receives source code-level information to estimate per memory-access instruction, per data array, and per kernel localities within GPU kernels. VLAG is only applicable to kernels with regular memory access patterns. VLAG was experimentally evaluated using an NVIDIA Maxwell GPU. For two different Matrix Multiplication kernels, the average errors of 7.68% and 6.29%, was resulted, respectively. The slowdown of VLAG for MM was measured 1.4X which, comparing with other approaches such as trace-driven simulation, is negligible.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121658653","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: 6
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