Pub Date : 2014-10-01DOI: 10.1109/ICACSIS.2014.7065860
Agus Widodo, I. Budi, B. Widjaja
Machine Learning methods such as Neural Network (NN) and Support Vector Regression (SVR) have been studied extensively for time series forecasting. Multiple Kernel Learning (MKL) which utilizes SVR as the predictor is yet another recent approaches to choose suitable kernels from a given pool of kernels by means of a linear combination of some base kernels. However, some literatures suggest that this linear combination of kernels cannot consistently outperform either the uniform combination of base kernels or simply the best single kernel. Hence, in this paper, other combination method is devised, namely the squared combination of base kernels, which gives more weight on suitable kernels and vice versa. We use time series data having various length, pattern and horizons, namely the 111 time series from NN3 competition, 3003 of M3 competition, 1001 of Ml competition and reduced 111 of Ml competition. Our experimental results indicate that our new forecasting approaches using squared combination of Multiple Kernel Learning (MKL) may perform well compared to the other methods on the same dataset.
{"title":"Nonlinearly weighted multiple kernel learning for time series forecasting","authors":"Agus Widodo, I. Budi, B. Widjaja","doi":"10.1109/ICACSIS.2014.7065860","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065860","url":null,"abstract":"Machine Learning methods such as Neural Network (NN) and Support Vector Regression (SVR) have been studied extensively for time series forecasting. Multiple Kernel Learning (MKL) which utilizes SVR as the predictor is yet another recent approaches to choose suitable kernels from a given pool of kernels by means of a linear combination of some base kernels. However, some literatures suggest that this linear combination of kernels cannot consistently outperform either the uniform combination of base kernels or simply the best single kernel. Hence, in this paper, other combination method is devised, namely the squared combination of base kernels, which gives more weight on suitable kernels and vice versa. We use time series data having various length, pattern and horizons, namely the 111 time series from NN3 competition, 3003 of M3 competition, 1001 of Ml competition and reduced 111 of Ml competition. Our experimental results indicate that our new forecasting approaches using squared combination of Multiple Kernel Learning (MKL) may perform well compared to the other methods on the same dataset.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127072910","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 : 2014-10-01DOI: 10.1109/ICACSIS.2014.7065829
P. Sauvage, A. Courtin, P.A Bonneau, K. Chauffeur, V. Claisse
With emergence of multi-agent systems, there is a need to invent new models to represent them. Petri nets are good candidates for representation of interaction and change of state. However, their limitations do not allow them to be relevant for modeling in software engineering development. Here, a new mathematical model incorporating the same graphical conventions and providing backward compatibility with the canonical model has been formalized. A practical application of the model is proposed to present the various features detailed in the analysis.
{"title":"An extension of Petri network for multi-agent system representation","authors":"P. Sauvage, A. Courtin, P.A Bonneau, K. Chauffeur, V. Claisse","doi":"10.1109/ICACSIS.2014.7065829","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065829","url":null,"abstract":"With emergence of multi-agent systems, there is a need to invent new models to represent them. Petri nets are good candidates for representation of interaction and change of state. However, their limitations do not allow them to be relevant for modeling in software engineering development. Here, a new mathematical model incorporating the same graphical conventions and providing backward compatibility with the canonical model has been formalized. A practical application of the model is proposed to present the various features detailed in the analysis.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125079227","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 : 2014-10-01DOI: 10.1109/ICACSIS.2014.7065884
M. Suryani, H. Santoso, Z. Hasibuan
One of the emerging issue in e-learning is to create adaptive learning based on learner's perspective. Adaptive learning can be realized through personalization of e-learning. Personalized learning help learners to use their best performance in order to reach learning goals based on their needs, preferences, and characteristics. To accomodate different characteristics of the learners, learning content personalization system based on triple-factor learning type was developed. The characteristics of 36 triple-factor learning type were used as input for learning content personalization algorithm to produce learning content that suitable for the learners's learning type. The algorithm implemented into a system which called SCELE-Personalization Dynamic E-learning. The system was used by 118 learners in Science Writing course at the Faculty of Computer Science, Universitas Indonesia as experimental group. In order to find the best learning performance, the exam score from experiment group were compared with the exam score from control group. The result shows learning performance of experimental group that used personalized learning feature is better than learning performance of control group who used non-personalized learning feature. It can be seen from significant value (p<;0,05) and the different mean score of the experimental group that reach 13,68.
{"title":"Learning content personalization based on triple-factor learning type approach in e-learning","authors":"M. Suryani, H. Santoso, Z. Hasibuan","doi":"10.1109/ICACSIS.2014.7065884","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065884","url":null,"abstract":"One of the emerging issue in e-learning is to create adaptive learning based on learner's perspective. Adaptive learning can be realized through personalization of e-learning. Personalized learning help learners to use their best performance in order to reach learning goals based on their needs, preferences, and characteristics. To accomodate different characteristics of the learners, learning content personalization system based on triple-factor learning type was developed. The characteristics of 36 triple-factor learning type were used as input for learning content personalization algorithm to produce learning content that suitable for the learners's learning type. The algorithm implemented into a system which called SCELE-Personalization Dynamic E-learning. The system was used by 118 learners in Science Writing course at the Faculty of Computer Science, Universitas Indonesia as experimental group. In order to find the best learning performance, the exam score from experiment group were compared with the exam score from control group. The result shows learning performance of experimental group that used personalized learning feature is better than learning performance of control group who used non-personalized learning feature. It can be seen from significant value (p<;0,05) and the different mean score of the experimental group that reach 13,68.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125944344","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 : 2014-10-01DOI: 10.1109/ICACSIS.2014.7065854
L. S. Hasibuan, W. Kusuma, Willy Bayuardi Suwamo
The advance of DNA sequencing technology presents a significant bioinformatic challenges in a downstream analysis such as identification of single nucleotide polymorphism (SNP). SNP is the most abundant form of genetic marker and have been one of the most crucial researches in bioinformatics. SNP has been applied in wide area, but analysis of SNP in plants is very limited, as in cultivated soybean (Glycine max L.). This paper discusses the identification of SNP in cultivated soybean using Support Vector Machine (SVM). SVM is trained using positive and negative SNP. Previously, we performed a balancing positive and negative SNP with undersampling and oversampling to obtain training data. As a result, the model which is trained with balanced data has better performance than that with imbalanced data.
DNA测序技术的进步给下游分析带来了重大的生物信息学挑战，如单核苷酸多态性(SNP)的鉴定。SNP是最丰富的遗传标记形式，已成为生物信息学研究的重要内容之一。SNP已被广泛应用，但对植物SNP的分析非常有限，如对栽培大豆(Glycine max L.)的分析。本文讨论了利用支持向量机(SVM)识别栽培大豆SNP的方法。支持向量机使用正、负SNP进行训练。之前，我们通过欠采样和过采样来平衡正、负SNP以获得训练数据。结果表明，使用平衡数据训练的模型比使用不平衡数据训练的模型具有更好的性能。
{"title":"Identification of single nucleotide polymorphism using support vector machine on imbalanced data","authors":"L. S. Hasibuan, W. Kusuma, Willy Bayuardi Suwamo","doi":"10.1109/ICACSIS.2014.7065854","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065854","url":null,"abstract":"The advance of DNA sequencing technology presents a significant bioinformatic challenges in a downstream analysis such as identification of single nucleotide polymorphism (SNP). SNP is the most abundant form of genetic marker and have been one of the most crucial researches in bioinformatics. SNP has been applied in wide area, but analysis of SNP in plants is very limited, as in cultivated soybean (Glycine max L.). This paper discusses the identification of SNP in cultivated soybean using Support Vector Machine (SVM). SVM is trained using positive and negative SNP. Previously, we performed a balancing positive and negative SNP with undersampling and oversampling to obtain training data. As a result, the model which is trained with balanced data has better performance than that with imbalanced data.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121432683","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 : 2014-10-01DOI: 10.1109/ICACSIS.2014.7065890
Darius Andana Haris
Attacking is one of the popular tactics usually chosen by a soccer coach. With attacking effectively, chances to score goals can be enhanced as many as possible. Better attack needs good ball passing, and that is the purpose and focus of this research. Previous robotic soccer simulations employed simple weighting method with simple criteria and does not produce optimal ball passing. To overcome this problem this research proposes a set of criteria representing a more realistic situation. This set of criteria is formulated in and appropriate objective function which is optimized using pareto frontier and Normalized Normal Constraint. From the result of this experiment, it can be seen that this proposed method has a realibility of 20% higher that that of the previous method. And it has a better passing success rate with a 75% ball possession. Rather than that of the previous method with a 25% ball possession.
{"title":"Pareto frontier optimization in soccer simulation using normalized normal constraint","authors":"Darius Andana Haris","doi":"10.1109/ICACSIS.2014.7065890","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065890","url":null,"abstract":"Attacking is one of the popular tactics usually chosen by a soccer coach. With attacking effectively, chances to score goals can be enhanced as many as possible. Better attack needs good ball passing, and that is the purpose and focus of this research. Previous robotic soccer simulations employed simple weighting method with simple criteria and does not produce optimal ball passing. To overcome this problem this research proposes a set of criteria representing a more realistic situation. This set of criteria is formulated in and appropriate objective function which is optimized using pareto frontier and Normalized Normal Constraint. From the result of this experiment, it can be seen that this proposed method has a realibility of 20% higher that that of the previous method. And it has a better passing success rate with a 75% ball possession. Rather than that of the previous method with a 25% ball possession.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124028974","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 : 2014-10-01DOI: 10.1109/ICACSIS.2014.7065898
I. P. Satwika, I. Habibie, M. A. Ma'sum, A. Febrian, E. Budianto
One of the most profound use of ultrasound imaging is to generate the image of fetal during pregnancy. This paper will describe an ellipse detection approach to automatically detect and approximate the head size of the fetal. The method was developed using the Hough Transform techniques that have been modified and optimized by Particle Swarm Optimization (PSO). Experiments of the method are tested on synthetic and real ellipse image dataset. For real images, the detection was applied on 2D ultrasonography images to perform fetal head measurement to approximate the Head Circumference (HC) and Biparietal Diameter (BPD). Experiment result showed that the proposed method can perform ellipse detection in synthetic dataset with satisfactory result for noisy images with noise density up to 0.4 and able to perform the fetal head detection for real images with an averate hit rate of 0.654. This proposed method can also perform detection on images that have high degree of noise or incomplete ellipse images generated from the fetal objects.
{"title":"Particle swarm optimation based 2-dimensional randomized hough transform for fetal head biometry detection and approximation in ultrasound imaging","authors":"I. P. Satwika, I. Habibie, M. A. Ma'sum, A. Febrian, E. Budianto","doi":"10.1109/ICACSIS.2014.7065898","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065898","url":null,"abstract":"One of the most profound use of ultrasound imaging is to generate the image of fetal during pregnancy. This paper will describe an ellipse detection approach to automatically detect and approximate the head size of the fetal. The method was developed using the Hough Transform techniques that have been modified and optimized by Particle Swarm Optimization (PSO). Experiments of the method are tested on synthetic and real ellipse image dataset. For real images, the detection was applied on 2D ultrasonography images to perform fetal head measurement to approximate the Head Circumference (HC) and Biparietal Diameter (BPD). Experiment result showed that the proposed method can perform ellipse detection in synthetic dataset with satisfactory result for noisy images with noise density up to 0.4 and able to perform the fetal head detection for real images with an averate hit rate of 0.654. This proposed method can also perform detection on images that have high degree of noise or incomplete ellipse images generated from the fetal objects.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125578470","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 : 2014-10-01DOI: 10.1109/ICACSIS.2014.7065896
Syandra Sari, M. Adriani
One important task in cross-language information retrieval (CLIR) is to determine the relevance of a document from a number of documents based on user query. In this paper we applied pointwise learning to rank in SVM (Support Vector Machine) to determine the relevance of a document and used BM25 (Best Match 25) ranking function for selecting words as features. We did the experiment in Indonesian-English CLIR The results show an average ability of SVM to identify relevant documents is 88.51%, while the average accuracy of SVM to identify non relevant documents is 88%.
{"title":"Learning to rank for determining relevant document in Indonesian-English cross language information retrieval using BM25","authors":"Syandra Sari, M. Adriani","doi":"10.1109/ICACSIS.2014.7065896","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065896","url":null,"abstract":"One important task in cross-language information retrieval (CLIR) is to determine the relevance of a document from a number of documents based on user query. In this paper we applied pointwise learning to rank in SVM (Support Vector Machine) to determine the relevance of a document and used BM25 (Best Match 25) ranking function for selecting words as features. We did the experiment in Indonesian-English CLIR The results show an average ability of SVM to identify relevant documents is 88.51%, while the average accuracy of SVM to identify non relevant documents is 88%.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"2018 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125755484","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 : 2014-10-01DOI: 10.1109/ICACSIS.2014.7065825
Audrey Bona, Jean-Marc Salotti
This paper presents the results of an experiment conducted that aim at bringing natural and easiest, the interaction between the occupants of a green building and the building itself. The true challenge is to create buildings that can be understood and used in an optimal way without adding new constraints and reducing comfort of the user. We've based our work on the concept of affordance as a way of adapting the building to their occupant's behaviour. The results of the proposed experiment shows that simple behavioural patterns should be taken into account to better apprehend the interaction between users and their houses. This experiment allowed us to highlight these patterns. Data was used to determine the weightings of criteria identified as impact factors on user's practices. By integrating these weights in our model of interaction we'll be able to assess the level of compatibility between the users and the building and have some possible solutions to increase it.
{"title":"Interaction between users and buildings: Results of a multicreteria analysis","authors":"Audrey Bona, Jean-Marc Salotti","doi":"10.1109/ICACSIS.2014.7065825","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065825","url":null,"abstract":"This paper presents the results of an experiment conducted that aim at bringing natural and easiest, the interaction between the occupants of a green building and the building itself. The true challenge is to create buildings that can be understood and used in an optimal way without adding new constraints and reducing comfort of the user. We've based our work on the concept of affordance as a way of adapting the building to their occupant's behaviour. The results of the proposed experiment shows that simple behavioural patterns should be taken into account to better apprehend the interaction between users and their houses. This experiment allowed us to highlight these patterns. Data was used to determine the weightings of criteria identified as impact factors on user's practices. By integrating these weights in our model of interaction we'll be able to assess the level of compatibility between the users and the building and have some possible solutions to increase it.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"803 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134039635","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 : 2014-10-01DOI: 10.1109/ICACSIS.2014.7065880
Cliffen Allen, Jeanny Pragantha, Darius Andana Haris
Some people want to own a pet, but sometimes they can't because of some reasons that prevents them to own one. In this paper we are trying to list some of the causes that prevents people from take on a challenge to raise a pet that drives us to make this game to gives player an experience in raising a pet, we also want to take this opportunity to show the workflow in making this game which start from completing the concept, making 3D models, scripting, and integrating augmented reality as our main feature in this game to provide unique experience in playing our game.
{"title":"3D virtual pet game \"Moar\" with augmented reality to simulate pet raising scenario on mobile device","authors":"Cliffen Allen, Jeanny Pragantha, Darius Andana Haris","doi":"10.1109/ICACSIS.2014.7065880","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065880","url":null,"abstract":"Some people want to own a pet, but sometimes they can't because of some reasons that prevents them to own one. In this paper we are trying to list some of the causes that prevents people from take on a challenge to raise a pet that drives us to make this game to gives player an experience in raising a pet, we also want to take this opportunity to show the workflow in making this game which start from completing the concept, making 3D models, scripting, and integrating augmented reality as our main feature in this game to provide unique experience in playing our game.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131688092","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 : 2014-10-01DOI: 10.1109/ICACSIS.2014.7065889
D. Herwindiati, S. M. Isa, J. Hendryli
The robust dimension reduction for classification of two dimensional data is discussed in this paper. The classification process is done with reference of original data. The classifying of class membership is not easy when more than one variable are loaded with the same information, and they can be written as a near linear combination of other variables. The standard approach to overcome this problem is dimension reduction. One of the most common forms of dimensionality reduction is the principal component analysis (PCA). The two-dimensional principal component (2DPCA) is often called a variant of principal component. The image matrices were directly treated as 2D matrices; the covariance matrix of image can be constructed directly using the original image matrices. The presence of outliers in the data has been proved to pose a serious problem in dimension reduction. The first component consisting of the greatest variation is often pushed toward the anomalous observations. The robust minimizing vector variance (MW) combined with two dimensional projection approach is used for solving the problem. The computation experiment shows the robust method has the good performances for matrix data classification.
{"title":"Performance of robust two-dimensional principal component for classification","authors":"D. Herwindiati, S. M. Isa, J. Hendryli","doi":"10.1109/ICACSIS.2014.7065889","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065889","url":null,"abstract":"The robust dimension reduction for classification of two dimensional data is discussed in this paper. The classification process is done with reference of original data. The classifying of class membership is not easy when more than one variable are loaded with the same information, and they can be written as a near linear combination of other variables. The standard approach to overcome this problem is dimension reduction. One of the most common forms of dimensionality reduction is the principal component analysis (PCA). The two-dimensional principal component (2DPCA) is often called a variant of principal component. The image matrices were directly treated as 2D matrices; the covariance matrix of image can be constructed directly using the original image matrices. The presence of outliers in the data has been proved to pose a serious problem in dimension reduction. The first component consisting of the greatest variation is often pushed toward the anomalous observations. The robust minimizing vector variance (MW) combined with two dimensional projection approach is used for solving the problem. The computation experiment shows the robust method has the good performances for matrix data classification.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115780183","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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