Pub Date : 2017-08-01DOI: 10.1109/IC3.2017.8284355
Kartikeya Jaiswal, H. Mittal, Sonia Kukreja
Meta-heuristic algorithms are quiet widely used in engineering and science. In this paper, a novel variant of grey-wolf optimizer, randomized gray-wolf optimizer, is proposed where the effect of random coefficients of the best agents are taken into consideration. The proposed variant is tested on a set of 12 standard benchmark functions and compared with grey-wolf optimizer and particle swarm optimization on different dimensional search spaces. To statistically validate and analyse the convergence behaviour of variant, wilcoxon rank sum test along with convergence graphs over 30 runs is studied. The experimental study signify that the proposed variant not only enhances the performance but has comparable convergence rate, especially in case of multi-modal problems.
{"title":"Randomized grey wolf optimizer (RGWO) with randomly weighted coefficients","authors":"Kartikeya Jaiswal, H. Mittal, Sonia Kukreja","doi":"10.1109/IC3.2017.8284355","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284355","url":null,"abstract":"Meta-heuristic algorithms are quiet widely used in engineering and science. In this paper, a novel variant of grey-wolf optimizer, randomized gray-wolf optimizer, is proposed where the effect of random coefficients of the best agents are taken into consideration. The proposed variant is tested on a set of 12 standard benchmark functions and compared with grey-wolf optimizer and particle swarm optimization on different dimensional search spaces. To statistically validate and analyse the convergence behaviour of variant, wilcoxon rank sum test along with convergence graphs over 30 runs is studied. The experimental study signify that the proposed variant not only enhances the performance but has comparable convergence rate, especially in case of multi-modal problems.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123526571","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284309
Munaza Ramzan, S. Mehta, E. Annapoorna
A medium with a huge amount of information like Twitter, can be analyzed for surveying the opinions or sentiments of the public towards future leaders. This paper focuses on predicting the winning party in UP elections with the help of public opinion on Twitter. The paper intends to understand whether tweets can be used as useful method in predicting the election results or is it just a social hype. Twitter API is used to extract tweets about the UP elections. Public opinion is then ascertained with the help of sentiment analysis on the tweets. After sufficient amount of tweets are collected for analysis, we are simply expanding on positive tweets and getting 45.13% for BJP, the highest percentage as compared to other parties. On comparing this result with the exit polls and the actual election results, the prediction of winning party by the twitter data analysis is correct.
{"title":"Are tweets the real estimators of election results?","authors":"Munaza Ramzan, S. Mehta, E. Annapoorna","doi":"10.1109/IC3.2017.8284309","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284309","url":null,"abstract":"A medium with a huge amount of information like Twitter, can be analyzed for surveying the opinions or sentiments of the public towards future leaders. This paper focuses on predicting the winning party in UP elections with the help of public opinion on Twitter. The paper intends to understand whether tweets can be used as useful method in predicting the election results or is it just a social hype. Twitter API is used to extract tweets about the UP elections. Public opinion is then ascertained with the help of sentiment analysis on the tweets. After sufficient amount of tweets are collected for analysis, we are simply expanding on positive tweets and getting 45.13% for BJP, the highest percentage as compared to other parties. On comparing this result with the exit polls and the actual election results, the prediction of winning party by the twitter data analysis is correct.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"424 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122798309","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284339
Amanpreet Kaur, Padam Kumar, Govind P. Gupta
Localization is hot research topic in the area of WSNs. Many proposals have come in this area to develop an efficient, accurate, scalable and feasible algorithm. Distance-Vector (DV-Hop) is one standout amongst the most prominent algorithms due to its advantages, but it has still low accuracy. To improve accuracy of DV-Hop algorithm, the improvement has been proposed which combines a non-linear numerical method with DV-Hop. This improvement achieves better accuracy as it uses non-linear method for non-linear localization algorithm.
{"title":"Impact of non-linear numerical methods on localization algorithm","authors":"Amanpreet Kaur, Padam Kumar, Govind P. Gupta","doi":"10.1109/IC3.2017.8284339","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284339","url":null,"abstract":"Localization is hot research topic in the area of WSNs. Many proposals have come in this area to develop an efficient, accurate, scalable and feasible algorithm. Distance-Vector (DV-Hop) is one standout amongst the most prominent algorithms due to its advantages, but it has still low accuracy. To improve accuracy of DV-Hop algorithm, the improvement has been proposed which combines a non-linear numerical method with DV-Hop. This improvement achieves better accuracy as it uses non-linear method for non-linear localization algorithm.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116777027","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284331
J. Chauhan, A. Goel
Massive Open Online Course (MOOC) is a popular way for offering online courses, globally, by the education providers. With large number of learners enrolled in the courses, it becomes difficult to help the learners, individually, in achieving learning objectives. But, large amount of data gets generated from activities performed by the learner. Learning analytics uses the learner-generated educational data to improve the learning process. Currently, MOOCs provide limited support to the learners for learning analytics. In this paper, an analysis of features offered to the learners by different MOOC platforms for learning analytics, is presented. For this, features of several key platforms are identified and further categorized for analysis purposes. The comparative analysis helps during selection of a platform on basis of the analytical features supported by them. Also, a recommendation is provided for enhancement of learning analytics capabilities of MOOC platforms.
{"title":"A feature-based analysis of MOOC for learning analytics","authors":"J. Chauhan, A. Goel","doi":"10.1109/IC3.2017.8284331","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284331","url":null,"abstract":"Massive Open Online Course (MOOC) is a popular way for offering online courses, globally, by the education providers. With large number of learners enrolled in the courses, it becomes difficult to help the learners, individually, in achieving learning objectives. But, large amount of data gets generated from activities performed by the learner. Learning analytics uses the learner-generated educational data to improve the learning process. Currently, MOOCs provide limited support to the learners for learning analytics. In this paper, an analysis of features offered to the learners by different MOOC platforms for learning analytics, is presented. For this, features of several key platforms are identified and further categorized for analysis purposes. The comparative analysis helps during selection of a platform on basis of the analytical features supported by them. Also, a recommendation is provided for enhancement of learning analytics capabilities of MOOC platforms.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116283151","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284315
Nagaratna B. Chittaragi, S. Koolagudi
In this paper, word based dialect classification system is proposed by using acoustic characteristics of the speech signal. Dialects mainly represent the different pronunciation patterns of any language. Dialectal cues can exist at various levels such as phoneme, syllable, word, sentence and phrase in an utterance. Word level dialectal traits are extracted to recognize dialects since every word exhibits significant dialect discriminating cues. Intonational Variations in English (IViE) speech corpus recorded in British English has been considered. The corpus includes nine dialects which cover nine distinct regions of British Isles. Acoustic properties such as spectral and prosodic features are derived from word level to construct the feature vector. Further, two different classification algorithms such as support vector machine (SVM) and tree-based extreme gradient boosting (XGB) ensemble algorithms are used to extract the prominent patterns that are used to discriminate the dialects. From the experiments, a better performance has been observed with word level traits using ensemble methods over the SVM classification method.
{"title":"Acoustic features based word level dialect classification using SVM and ensemble methods","authors":"Nagaratna B. Chittaragi, S. Koolagudi","doi":"10.1109/IC3.2017.8284315","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284315","url":null,"abstract":"In this paper, word based dialect classification system is proposed by using acoustic characteristics of the speech signal. Dialects mainly represent the different pronunciation patterns of any language. Dialectal cues can exist at various levels such as phoneme, syllable, word, sentence and phrase in an utterance. Word level dialectal traits are extracted to recognize dialects since every word exhibits significant dialect discriminating cues. Intonational Variations in English (IViE) speech corpus recorded in British English has been considered. The corpus includes nine dialects which cover nine distinct regions of British Isles. Acoustic properties such as spectral and prosodic features are derived from word level to construct the feature vector. Further, two different classification algorithms such as support vector machine (SVM) and tree-based extreme gradient boosting (XGB) ensemble algorithms are used to extract the prominent patterns that are used to discriminate the dialects. From the experiments, a better performance has been observed with word level traits using ensemble methods over the SVM classification method.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122420834","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284352
Shiraaz Saad, H. Kumar, T. K. Tewari
The volume of the image databases have increased to such an extent that it is very difficult to retrieve the relevant images from the database efficiently. Content Based Image Retrieval methods use shape, color and texture features of the images and gives a more accurate method to search for the relevant images in the database. In this paper, we propose an approach for an efficient Content Based Image Retrieval which combines the use of Support Vector Machine (SVM) with color histogram. A two level approach is used by first filtering the relevant images using SVM followed by color based matching. The comparison of proposed image retrieval techniques is done based on the retrieval time and accuracy of the retrieved images.
{"title":"Efficient content based image retrieval using SVM and color histogram","authors":"Shiraaz Saad, H. Kumar, T. K. Tewari","doi":"10.1109/IC3.2017.8284352","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284352","url":null,"abstract":"The volume of the image databases have increased to such an extent that it is very difficult to retrieve the relevant images from the database efficiently. Content Based Image Retrieval methods use shape, color and texture features of the images and gives a more accurate method to search for the relevant images in the database. In this paper, we propose an approach for an efficient Content Based Image Retrieval which combines the use of Support Vector Machine (SVM) with color histogram. A two level approach is used by first filtering the relevant images using SVM followed by color based matching. The comparison of proposed image retrieval techniques is done based on the retrieval time and accuracy of the retrieved images.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115968566","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284323
M. Kumar, Indrajeet Gupta, P. K. Jana
Workflow scheduling has been widely adopted as a deep concern for heterogeneous cloud computing environment and recognized as a well-known NP-complete problem. The scheduling goal of any workflow scheduler is to map the user requests on the available virtual machines (VMs) which are deployed on various cloud servers such that overall processing time and incurred cost is minimized. This paper proposes a delay-based workflow scheduling algorithm for cost optimization in the heterogeneous cloud system. The proposed scheduling heuristic works in two phases. In the first phase, the proposed heuristic introduces a new priority scheme for the tasks. The prioritized tasks are then assigned to the appropriate VMs in the next phase. The algorithm is simulated on various synthetic workflows and two benchmark scientific workflows with different range of tasks. The experimental results of the proposed algorithm supercedes the existing scheduling algorithms in terms of makespan, schedule length ratio (SLR) and cost.
{"title":"Delay-based workflow scheduling for cost optimization in heterogeneous cloud system","authors":"M. Kumar, Indrajeet Gupta, P. K. Jana","doi":"10.1109/IC3.2017.8284323","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284323","url":null,"abstract":"Workflow scheduling has been widely adopted as a deep concern for heterogeneous cloud computing environment and recognized as a well-known NP-complete problem. The scheduling goal of any workflow scheduler is to map the user requests on the available virtual machines (VMs) which are deployed on various cloud servers such that overall processing time and incurred cost is minimized. This paper proposes a delay-based workflow scheduling algorithm for cost optimization in the heterogeneous cloud system. The proposed scheduling heuristic works in two phases. In the first phase, the proposed heuristic introduces a new priority scheme for the tasks. The prioritized tasks are then assigned to the appropriate VMs in the next phase. The algorithm is simulated on various synthetic workflows and two benchmark scientific workflows with different range of tasks. The experimental results of the proposed algorithm supercedes the existing scheduling algorithms in terms of makespan, schedule length ratio (SLR) and cost.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124126309","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284326
Chetna Gupta, Arpita Jadhav Bhatt, S. Mittal
There are many mobile operating systems for different smartphones. iOS is one of them available exclusively on Apple devices. It offers various features such as GPS tracking, Siri, multi touch gestures, biometric authentication etc. The functionality of an iOS device can be expanded by installing third party applications, available from App store. Apple follows a mandatory code signing process to ensure that applications are developed by trusted enterprises/developers and no malware exists on App Store. However, past attacks on iOS devices such as ikee, Wirelurker have revealed that Apple's code signing process is not sufficient to ensure the security of its devices. Considering past attacks on iOS devices and with view of analyzing the behavior of iOS applications, this paper aims to describe how an analyst can perform reverse engineering of iOS applications by disassembling, decompiling and debugging application's code using Disassembler to discover the vulnerable areas.
{"title":"Disassembling and patching iOS applications at assembly level","authors":"Chetna Gupta, Arpita Jadhav Bhatt, S. Mittal","doi":"10.1109/IC3.2017.8284326","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284326","url":null,"abstract":"There are many mobile operating systems for different smartphones. iOS is one of them available exclusively on Apple devices. It offers various features such as GPS tracking, Siri, multi touch gestures, biometric authentication etc. The functionality of an iOS device can be expanded by installing third party applications, available from App store. Apple follows a mandatory code signing process to ensure that applications are developed by trusted enterprises/developers and no malware exists on App Store. However, past attacks on iOS devices such as ikee, Wirelurker have revealed that Apple's code signing process is not sufficient to ensure the security of its devices. Considering past attacks on iOS devices and with view of analyzing the behavior of iOS applications, this paper aims to describe how an analyst can perform reverse engineering of iOS applications by disassembling, decompiling and debugging application's code using Disassembler to discover the vulnerable areas.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126177927","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284298
Seba Susan, Amandeep Kaur
Recognizing the emotional state of a human being from his/her speech is of great significance in modern surveillance systems. The Mel-Frequency Cepstral Coefficients (MFCC), pitch and energy are conventional speech cues that have been linked to emotions since long. In our work, we measure the randomness of these cues over time for discriminating between various human emotions. Entropy is used for measuring the randomness of the cues, computed from temporal histograms as well as temporal co-occurrence matrices. The direct values of MFCC, pitch and energy are not included and only their randomness is considered, since the actual values of MFCC, pitch and energy are often a characteristic of the speaker as much as of the emotion involved. The new set of entropy features for speech based emotion recognition is compared for its efficiency with the state-of-the- art methods on the benchmark SAVEE database. The higher classification accuracies demonstrate the efficiency of our approach.
{"title":"Measuring the randomness of speech cues for emotion recognition","authors":"Seba Susan, Amandeep Kaur","doi":"10.1109/IC3.2017.8284298","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284298","url":null,"abstract":"Recognizing the emotional state of a human being from his/her speech is of great significance in modern surveillance systems. The Mel-Frequency Cepstral Coefficients (MFCC), pitch and energy are conventional speech cues that have been linked to emotions since long. In our work, we measure the randomness of these cues over time for discriminating between various human emotions. Entropy is used for measuring the randomness of the cues, computed from temporal histograms as well as temporal co-occurrence matrices. The direct values of MFCC, pitch and energy are not included and only their randomness is considered, since the actual values of MFCC, pitch and energy are often a characteristic of the speaker as much as of the emotion involved. The new set of entropy features for speech based emotion recognition is compared for its efficiency with the state-of-the- art methods on the benchmark SAVEE database. The higher classification accuracies demonstrate the efficiency of our approach.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130498478","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}
With online information thefts increasing every day, enhanced user authentication systems have become a necessity. This is due to the fact that current authentication systems have certain weaknesses, such as the username used for login is easily known to anyone and password can be guessed or forcibly retrieved. This paper focuses on overcoming these weaknesses by recognizing users based on their typing patterns. This technique which is used to uniquely label users based on their typing rhythm is known as keystroke dynamics. In an authentication system employing keystroke dynamics, samples provided by the users during the initial sign up phase are used to generate a template for the future authentication. This template is regularly updated whenever the user is correctly identified. The work proposed in this paper focuses on implementing a keystroke dynamics based authentication system using fuzzy logic. It also discusses how a fuzzy logic approach is beneficial as compared to other approaches implemented previously, and how a continuous learning model improves accuracy over time. The proposed fuzzy logic model gives 98% authentication accuracy and 2% false rejection rate.
{"title":"Keystroke dynamics based authentication using fuzzy logic","authors":"Jatin Yadav, Kavita Pandey, Shashank Gupta, Richa Sharma","doi":"10.1109/IC3.2017.8284338","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284338","url":null,"abstract":"With online information thefts increasing every day, enhanced user authentication systems have become a necessity. This is due to the fact that current authentication systems have certain weaknesses, such as the username used for login is easily known to anyone and password can be guessed or forcibly retrieved. This paper focuses on overcoming these weaknesses by recognizing users based on their typing patterns. This technique which is used to uniquely label users based on their typing rhythm is known as keystroke dynamics. In an authentication system employing keystroke dynamics, samples provided by the users during the initial sign up phase are used to generate a template for the future authentication. This template is regularly updated whenever the user is correctly identified. The work proposed in this paper focuses on implementing a keystroke dynamics based authentication system using fuzzy logic. It also discusses how a fuzzy logic approach is beneficial as compared to other approaches implemented previously, and how a continuous learning model improves accuracy over time. The proposed fuzzy logic model gives 98% authentication accuracy and 2% false rejection rate.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114887865","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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