Telmo Amaral, Luís M. Silva, Luís A. Alexandre, Chetak Kandaswamy, Jorge M. Santos, J. M. D. Sá
Deep neural networks comprise several hidden layers of units, which can be pre-trained one at a time via an unsupervised greedy approach. A whole network can then be trained (fine-tuned) in a supervised fashion. One possible pre-training strategy is to regard each hidden layer in the network as the input layer of an auto-encoder. Since auto-encoders aim to reconstruct their own input, their training must be based on some cost function capable of measuring reconstruction performance. Similarly, the supervised fine-tuning of a deep network needs to be based on some cost function that reflects prediction performance. In this work we compare different combinations of cost functions in terms of their impact on layer-wise reconstruction performance and on supervised classification performance of deep networks. We employed two classic functions, namely the cross-entropy (CE) cost and the sum of squared errors (SSE), as well as the exponential (EXP) cost, inspired by the error entropy concept. Our results were based on a number of artificial and real-world data sets.
{"title":"Using Different Cost Functions to Train Stacked Auto-Encoders","authors":"Telmo Amaral, Luís M. Silva, Luís A. Alexandre, Chetak Kandaswamy, Jorge M. Santos, J. M. D. Sá","doi":"10.1109/MICAI.2013.20","DOIUrl":"https://doi.org/10.1109/MICAI.2013.20","url":null,"abstract":"Deep neural networks comprise several hidden layers of units, which can be pre-trained one at a time via an unsupervised greedy approach. A whole network can then be trained (fine-tuned) in a supervised fashion. One possible pre-training strategy is to regard each hidden layer in the network as the input layer of an auto-encoder. Since auto-encoders aim to reconstruct their own input, their training must be based on some cost function capable of measuring reconstruction performance. Similarly, the supervised fine-tuning of a deep network needs to be based on some cost function that reflects prediction performance. In this work we compare different combinations of cost functions in terms of their impact on layer-wise reconstruction performance and on supervised classification performance of deep networks. We employed two classic functions, namely the cross-entropy (CE) cost and the sum of squared errors (SSE), as well as the exponential (EXP) cost, inspired by the error entropy concept. Our results were based on a number of artificial and real-world data sets.","PeriodicalId":340039,"journal":{"name":"2013 12th Mexican International Conference on Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116679175","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}
Alejandro Molina-Villegas, Juan-Manuel Torres-Moreno, E. SanJuan, Gerardo E Sierra, Julio Rojas-Mora
In this paper we revisit the Textual Energy model. We deal with the two major disadvantages of the Textual Energy: the asymmetry of the distribution and the unbounded ness of the maximum value. Although this model has been successfully used in several NLP tasks like summarization, clustering and sentence compression, no correction of these problems has been proposed until now. Concerning the maximum value, we analyze the computation of Textual Energy matrix and we conclude that energy values are dominated by the lexical richness in quadratic growth of the vocabulary size. Using the Box-Cox transformation, we show empirical evidence that a log transformation could correct both problems.
{"title":"Analysis and Transformation of Textual Energy Distribution","authors":"Alejandro Molina-Villegas, Juan-Manuel Torres-Moreno, E. SanJuan, Gerardo E Sierra, Julio Rojas-Mora","doi":"10.1109/MICAI.2013.32","DOIUrl":"https://doi.org/10.1109/MICAI.2013.32","url":null,"abstract":"In this paper we revisit the Textual Energy model. We deal with the two major disadvantages of the Textual Energy: the asymmetry of the distribution and the unbounded ness of the maximum value. Although this model has been successfully used in several NLP tasks like summarization, clustering and sentence compression, no correction of these problems has been proposed until now. Concerning the maximum value, we analyze the computation of Textual Energy matrix and we conclude that energy values are dominated by the lexical richness in quadratic growth of the vocabulary size. Using the Box-Cox transformation, we show empirical evidence that a log transformation could correct both problems.","PeriodicalId":340039,"journal":{"name":"2013 12th Mexican International Conference on Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128893417","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}
Artificial-intelligence (AI)-base pattern recognition is of particular interests to many scientific disciplines ranging from life science to engineering. Practical applications of pattern or object recognition methods are numerous but still encountering many problems including the inherent difficulty in computerized feature extraction and classification. This paper proposes a strategy for object recognition resembling the active template matching strategy in birds. Experimental results on several databases suggest that using the active vision processing can improve classification rates implemented with various classifiers.
{"title":"Bird-Like Information Processing for AI-based Pattern Recognition","authors":"T. Pham","doi":"10.1109/MICAI.2013.27","DOIUrl":"https://doi.org/10.1109/MICAI.2013.27","url":null,"abstract":"Artificial-intelligence (AI)-base pattern recognition is of particular interests to many scientific disciplines ranging from life science to engineering. Practical applications of pattern or object recognition methods are numerous but still encountering many problems including the inherent difficulty in computerized feature extraction and classification. This paper proposes a strategy for object recognition resembling the active template matching strategy in birds. Experimental results on several databases suggest that using the active vision processing can improve classification rates implemented with various classifiers.","PeriodicalId":340039,"journal":{"name":"2013 12th Mexican International Conference on Artificial Intelligence","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130696679","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}
Knowledge exaction and text representation are considered as the main concepts concerning organizations nowadays. The estimation of the semantic similarity between words provides a valuable method to enable the understanding of texts. In the field of biomedical domains, using Ontologies have been very effective due to their scalability and efficiency. The problem of extracting knowledge from huge amount of data is recorded as an issue in the medical sector. In this paper, we aim to improve knowledge representation by using MeSH Ontology on medical theses data by analyzing the similarity between the keywords within the theses data and keywords after using the MeSH ontology. As a result, we are able to better discover the commonalities between theses data and hence, improve the accuracy of the similarity estimation which in return improves the scientific research sector. Then, K-means cluster algorithm was applied to get the nearest departments that can work together based on medical ontology. Experimental evaluations using 4, 878 theses data set in the medical sector at Cairo University indicate that the proposed approach yields results that correlate more closely with human assessments than other by using the standard ontology (MeSH). Results show that using ontology correlates better, compared to related works, with the similarity assessments provided by experts in biomedicine.
{"title":"Evaluation of Semantic Similarity across MeSH Ontology: A Cairo University Thesis Mining Case Study","authors":"Heba Ayeldeen, A. Hassanien, A. Fahmy","doi":"10.1109/MICAI.2013.24","DOIUrl":"https://doi.org/10.1109/MICAI.2013.24","url":null,"abstract":"Knowledge exaction and text representation are considered as the main concepts concerning organizations nowadays. The estimation of the semantic similarity between words provides a valuable method to enable the understanding of texts. In the field of biomedical domains, using Ontologies have been very effective due to their scalability and efficiency. The problem of extracting knowledge from huge amount of data is recorded as an issue in the medical sector. In this paper, we aim to improve knowledge representation by using MeSH Ontology on medical theses data by analyzing the similarity between the keywords within the theses data and keywords after using the MeSH ontology. As a result, we are able to better discover the commonalities between theses data and hence, improve the accuracy of the similarity estimation which in return improves the scientific research sector. Then, K-means cluster algorithm was applied to get the nearest departments that can work together based on medical ontology. Experimental evaluations using 4, 878 theses data set in the medical sector at Cairo University indicate that the proposed approach yields results that correlate more closely with human assessments than other by using the standard ontology (MeSH). Results show that using ontology correlates better, compared to related works, with the similarity assessments provided by experts in biomedicine.","PeriodicalId":340039,"journal":{"name":"2013 12th Mexican International Conference on Artificial Intelligence","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133766305","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}
Most of the samples discovered are variations of known malicious programs and thus have similar structures, however, there is no method of malware classification that is completely effective. To address this issue, the approach proposed in this paper represents a malware in terms of a vector, in which each feature consists of the amount of APIs called from a Dynamic Link Library (DLL). To determine if this approach is useful to classify malware variants into the correct families, we employ Euclidean Distance and a Multilayer Perceptron with several learning algorithms. The experimental results are analyzed to determine which method works best with the approach. The experiments were conducted with a database that contains real samples of worms and trojans and show that is possible to classify malware variants using the number of functions imported per library. However, the accuracy varies depending on the method used for the classification.
{"title":"Malware Classification Using Euclidean Distance and Artificial Neural Networks","authors":"Lilia E. Gonzalez, R. Vázquez","doi":"10.1109/MICAI.2013.18","DOIUrl":"https://doi.org/10.1109/MICAI.2013.18","url":null,"abstract":"Most of the samples discovered are variations of known malicious programs and thus have similar structures, however, there is no method of malware classification that is completely effective. To address this issue, the approach proposed in this paper represents a malware in terms of a vector, in which each feature consists of the amount of APIs called from a Dynamic Link Library (DLL). To determine if this approach is useful to classify malware variants into the correct families, we employ Euclidean Distance and a Multilayer Perceptron with several learning algorithms. The experimental results are analyzed to determine which method works best with the approach. The experiments were conducted with a database that contains real samples of worms and trojans and show that is possible to classify malware variants using the number of functions imported per library. However, the accuracy varies depending on the method used for the classification.","PeriodicalId":340039,"journal":{"name":"2013 12th Mexican International Conference on Artificial Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131405452","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}
Here we propose an approach for developing a diagnosis system for mood disorders, such as depression and bipolar disorder, based on language analysis from speech and text. Our system is based on the Mood State Indicator algorithm (MSI) for real-time analysis of a patient's mental state. MSI is designed to give a quantitative measure of cognitive state based on axiological values and time orientation of lexical features. MSI's multi-layered analytic engine consists of multiple information processing modules to systematically retrieve, parse and process features of a patient's discourse. Gold standard clinical criteria will be used to match language analysis indicators to mood disorder diagnosis.
{"title":"Approach Towards a Natural Language Analysis for Diagnosing Mood Disorders and Comorbid Conditions","authors":"N. Howard","doi":"10.1109/MICAI.2013.50","DOIUrl":"https://doi.org/10.1109/MICAI.2013.50","url":null,"abstract":"Here we propose an approach for developing a diagnosis system for mood disorders, such as depression and bipolar disorder, based on language analysis from speech and text. Our system is based on the Mood State Indicator algorithm (MSI) for real-time analysis of a patient's mental state. MSI is designed to give a quantitative measure of cognitive state based on axiological values and time orientation of lexical features. MSI's multi-layered analytic engine consists of multiple information processing modules to systematically retrieve, parse and process features of a patient's discourse. Gold standard clinical criteria will be used to match language analysis indicators to mood disorder diagnosis.","PeriodicalId":340039,"journal":{"name":"2013 12th Mexican International Conference on Artificial Intelligence","volume":"7 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124278913","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}
Natural language quantifiers can be classified according to their semantic type in addition to their syntactic expression. Quantification in Natural language (NL) has two types, ambiguous quantification and Unambiguous quantification. Unambiguous quantification is very simple and also called exact quantification, but ambiguous quantification is complex and also called inexact quantification. Inexact quantifiers include "many, much, a lot of, several, some, any, a few, little, fewer, fewest, Less, greater, at least, at most, more, exactly". To identify the problems of Natural language Quantification, convert these Natural Language sentences into First order logic by attaching weights and classify these complex sentences by using Markov Logic.
自然语言的量词除了根据其句法表达，还可以根据其语义类型进行分类。自然语言中的量化有二义性量化和无二义性量化两种。无二义量化很简单，也叫精确量化，而模糊量化很复杂，也叫不精确量化。不精确量词包括“many, much, lot of，几，some, any, a few, little, fewer, fewest, Less, greater, least, at most, more, exactly”。为了识别自然语言量化的问题，通过附加权重将这些自然语言句子转换为一阶逻辑，并使用马尔可夫逻辑对这些复句进行分类。
{"title":"Quantifiers Types Resolution in NL Software Requirements","authors":"Mehreen Saba, Imran Sarwar Bajwa","doi":"10.1109/MICAI.2013.49","DOIUrl":"https://doi.org/10.1109/MICAI.2013.49","url":null,"abstract":"Natural language quantifiers can be classified according to their semantic type in addition to their syntactic expression. Quantification in Natural language (NL) has two types, ambiguous quantification and Unambiguous quantification. Unambiguous quantification is very simple and also called exact quantification, but ambiguous quantification is complex and also called inexact quantification. Inexact quantifiers include \"many, much, a lot of, several, some, any, a few, little, fewer, fewest, Less, greater, at least, at most, more, exactly\". To identify the problems of Natural language Quantification, convert these Natural Language sentences into First order logic by attaching weights and classify these complex sentences by using Markov Logic.","PeriodicalId":340039,"journal":{"name":"2013 12th Mexican International Conference on Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116518757","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}
Recently the parametric representation using cochlea behavior has been used in different studies related with Automatic Speech Recognition (ASR). That is because this important organ of the hearing in the mammalians is the principal element used to make a transduction of the sound pressure that is received by the ear. In this paper we show how the macro and micro mechanical model is used in ASR tasks. We used the values that Neely founded in his work, related with the macro and micro mechanical model, such as was named, to set the central frequencies of a bank filter to obtain parameters from the speech used in a similar form as MFCC were constructed. We propose a new approach that considers a new form to construct the bank filter in our parametric representation. Then we used this distribution of the bank filter to have a new representation of the speech in frequency domain. It is important indicate that MFCC parameters use Mel scale to create a bank filter where central frequencies of each filter is in function of the scale mentioned above. We used the response of the Neely's model behavior to create the central frequencies of the bank filter mentioned above, then we substitute the Mel scale function by another representation. We use the place theory, and we reach a 98.5% of performance, for a task that uses isolated digits pronounced by 5 different speakers. Neely's model was used because a set of parameters of the cochlea as mass, damping and stiffness, among others, when are substituted inside the model make the response obtained is closer than von Békésy proposed in his preliminary work about principle function of the cochlea.
{"title":"Using a Model of the Cochlea Based in the Micro and Macro Mechanical to Find Parameters for Automatic Speech Recognition","authors":"J. Rodríguez, Jose Francisco Reyes Saldana","doi":"10.1109/MICAI.2013.39","DOIUrl":"https://doi.org/10.1109/MICAI.2013.39","url":null,"abstract":"Recently the parametric representation using cochlea behavior has been used in different studies related with Automatic Speech Recognition (ASR). That is because this important organ of the hearing in the mammalians is the principal element used to make a transduction of the sound pressure that is received by the ear. In this paper we show how the macro and micro mechanical model is used in ASR tasks. We used the values that Neely founded in his work, related with the macro and micro mechanical model, such as was named, to set the central frequencies of a bank filter to obtain parameters from the speech used in a similar form as MFCC were constructed. We propose a new approach that considers a new form to construct the bank filter in our parametric representation. Then we used this distribution of the bank filter to have a new representation of the speech in frequency domain. It is important indicate that MFCC parameters use Mel scale to create a bank filter where central frequencies of each filter is in function of the scale mentioned above. We used the response of the Neely's model behavior to create the central frequencies of the bank filter mentioned above, then we substitute the Mel scale function by another representation. We use the place theory, and we reach a 98.5% of performance, for a task that uses isolated digits pronounced by 5 different speakers. Neely's model was used because a set of parameters of the cochlea as mass, damping and stiffness, among others, when are substituted inside the model make the response obtained is closer than von Békésy proposed in his preliminary work about principle function of the cochlea.","PeriodicalId":340039,"journal":{"name":"2013 12th Mexican International Conference on Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125411282","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}
M. Santibáñez, R. M. Valdovinos, Adrián Trueba, Eréndira Rendón Lara, R. Alejo, E. López
Over time, it has been found there is valuable information within the data sets generated into different areas. These large data sets required to be processed with any data mining technique to get the hidden knowledge inside them. Due to nowadays many of data sets are integrated with a big number of instances and they do not have any information that can describe them, is necessary to use data mining methods such as clustering so it can permit to lump together the data according to its characteristics. Although there are algorithms that have good results with small or medium size data sets, they can provide poor results when they work with large data sets. Due to above mentioned in this paper we propose to use different cluster validation methods to determine clustering quality, as its analysis, so at the same time to determine in an empiric way the more reliable rates for working with large data sets.
{"title":"Applicability of Cluster Validation Indexes for Large Data Sets","authors":"M. Santibáñez, R. M. Valdovinos, Adrián Trueba, Eréndira Rendón Lara, R. Alejo, E. López","doi":"10.1109/MICAI.2013.30","DOIUrl":"https://doi.org/10.1109/MICAI.2013.30","url":null,"abstract":"Over time, it has been found there is valuable information within the data sets generated into different areas. These large data sets required to be processed with any data mining technique to get the hidden knowledge inside them. Due to nowadays many of data sets are integrated with a big number of instances and they do not have any information that can describe them, is necessary to use data mining methods such as clustering so it can permit to lump together the data according to its characteristics. Although there are algorithms that have good results with small or medium size data sets, they can provide poor results when they work with large data sets. Due to above mentioned in this paper we propose to use different cluster validation methods to determine clustering quality, as its analysis, so at the same time to determine in an empiric way the more reliable rates for working with large data sets.","PeriodicalId":340039,"journal":{"name":"2013 12th Mexican International Conference on Artificial Intelligence","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128752236","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}
Alejandro Jarillo-Silva, O. Dominguez-Ramirez, J. A. Cruz-Tolentino, L. E. R. Velasco, Vicente Parra‐Vega
This paper presents the design, construction and implementation of a calligraphic platform with biomedical applications. This technological tool could be employed in physiotherapy to recover the loss of calligraphic abilities caused by common psychomotor disorders such as dyslexia and brain stroke. The experimental platform allow to define the motion performance (physical interaction variables), in particular on upper limbs. the patient is guided through the end effector of a haptic device; to this end is used a nonlinear control in closed loop with the human operator, with language symbols as a trajectory tracking. This allows that the user can be a passive human, so the control law designed is based on passivity theory and sliding mode to achieve stability and security in human machine interaction. The haptic system described, is designed to improve the physiotherapeutic tasks by supplying the motion measurement (position/velocity) and its errors. Preliminary tests using this novel system demonstrated a significative influence on regain functions in patients with psychomotor disorders.
{"title":"Kinesthetic Guided with Graphotherapeutic Purposes","authors":"Alejandro Jarillo-Silva, O. Dominguez-Ramirez, J. A. Cruz-Tolentino, L. E. R. Velasco, Vicente Parra‐Vega","doi":"10.1109/MICAI.2013.45","DOIUrl":"https://doi.org/10.1109/MICAI.2013.45","url":null,"abstract":"This paper presents the design, construction and implementation of a calligraphic platform with biomedical applications. This technological tool could be employed in physiotherapy to recover the loss of calligraphic abilities caused by common psychomotor disorders such as dyslexia and brain stroke. The experimental platform allow to define the motion performance (physical interaction variables), in particular on upper limbs. the patient is guided through the end effector of a haptic device; to this end is used a nonlinear control in closed loop with the human operator, with language symbols as a trajectory tracking. This allows that the user can be a passive human, so the control law designed is based on passivity theory and sliding mode to achieve stability and security in human machine interaction. The haptic system described, is designed to improve the physiotherapeutic tasks by supplying the motion measurement (position/velocity) and its errors. Preliminary tests using this novel system demonstrated a significative influence on regain functions in patients with psychomotor disorders.","PeriodicalId":340039,"journal":{"name":"2013 12th Mexican International Conference on Artificial Intelligence","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132406584","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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