Pub Date : 2019-04-01DOI: 10.4018/IJSSCI.2019040102
Mohamed Garoui
Building a safety model to make expert decisions is an approach to improve the safety of a system. The issue of safe modeling and analyzing such domain is still an open research field. Providing quantitative estimation of a system's safety is an interesting method to study system complexity. This article explores the author's current methods and proposes a new formal model for quantitative estimation based on a stochastic activity network (SAN). This model is built based on some failure modes that affect platoon vehicles.
{"title":"Safe-Platoon: A Formal Model for Safety Evaluation","authors":"Mohamed Garoui","doi":"10.4018/IJSSCI.2019040102","DOIUrl":"https://doi.org/10.4018/IJSSCI.2019040102","url":null,"abstract":"Building a safety model to make expert decisions is an approach to improve the safety of a system. The issue of safe modeling and analyzing such domain is still an open research field. Providing quantitative estimation of a system's safety is an interesting method to study system complexity. This article explores the author's current methods and proposes a new formal model for quantitative estimation based on a stochastic activity network (SAN). This model is built based on some failure modes that affect platoon vehicles.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133960385","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 : 2019-04-01DOI: 10.4018/IJSSCI.2019040103
Chen Sun, Chunping Li, Yan Zhu
The authors present a robust and extendable localization system for monocular images. To have both robustness toward noise factors and extendibility to unfamiliar scenes simultaneously, our system combines traditional content-based image retrieval structure with CNN feature extraction model to localize monocular images. The core model of the system is a deep CNN feature extraction model. The feature extraction model can map an image to a d-dimension space where image pairs in the real word have smaller Euclidean distances. The feature extraction model is achieved using a deep Convnet modified from GoogLeNet. A special way to train the feature extraction model is proposed in the article using localization results from Cambridge Landmarks dataset. Through experiments, it is shown that the system is robust to noise factors supported by high level CNN features. Furthermore, the authors show that the system has a powerful extendibility to other unfamiliar scenes supported by a feature extract model's generic property and structure.
{"title":"A Novel Convolutional Neural Network Based Localization System for Monocular Images","authors":"Chen Sun, Chunping Li, Yan Zhu","doi":"10.4018/IJSSCI.2019040103","DOIUrl":"https://doi.org/10.4018/IJSSCI.2019040103","url":null,"abstract":"The authors present a robust and extendable localization system for monocular images. To have both robustness toward noise factors and extendibility to unfamiliar scenes simultaneously, our system combines traditional content-based image retrieval structure with CNN feature extraction model to localize monocular images. The core model of the system is a deep CNN feature extraction model. The feature extraction model can map an image to a d-dimension space where image pairs in the real word have smaller Euclidean distances. The feature extraction model is achieved using a deep Convnet modified from GoogLeNet. A special way to train the feature extraction model is proposed in the article using localization results from Cambridge Landmarks dataset. Through experiments, it is shown that the system is robust to noise factors supported by high level CNN features. Furthermore, the authors show that the system has a powerful extendibility to other unfamiliar scenes supported by a feature extract model's generic property and structure.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132128679","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 : 2019-04-01DOI: 10.4018/IJSSCI.2019040104
Y. MohanRoopa, A. Reddy
Component-based software engineering focuses on the development and reuse of components. The component reuse depends on the storage and retrieval processes. This article presents the component repository model for the developers to achieve good productivity. The component selection from the component repository according to the functionality and requirements is a crucial process. This article proposed an algorithm for optimizing component selection with functionality constraints like customer size, reliability, and performance. The experimental result evaluates the performance of the algorithm.
{"title":"An Optimized Component Selection Algorithm for Self-Adaptive Software Architecture Using the Component Repository: Self-Adaptive Software Architecture","authors":"Y. MohanRoopa, A. Reddy","doi":"10.4018/IJSSCI.2019040104","DOIUrl":"https://doi.org/10.4018/IJSSCI.2019040104","url":null,"abstract":"Component-based software engineering focuses on the development and reuse of components. The component reuse depends on the storage and retrieval processes. This article presents the component repository model for the developers to achieve good productivity. The component selection from the component repository according to the functionality and requirements is a crucial process. This article proposed an algorithm for optimizing component selection with functionality constraints like customer size, reliability, and performance. The experimental result evaluates the performance of the algorithm.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"37 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116545691","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 : 2019-01-01DOI: 10.4018/IJSSCI.2019010102
Lei Zhang
In this research, artificial neural networks (ANN) with various architectures are trained to generate the chaotic time series patterns of the Lorenz attractor. The ANN training performance is evaluated based on the size and precision of the training data. The nonlinear Auto-Regressive (NAR) model is trained in open loop mode first. The trained model is then used with closed loop feedback to predict the chaotic time series outputs. The research goal is to use the designed NAR ANN model for the simulation and analysis of Electroencephalogram (EEG) signals in order to study brain activities. A simple ANN topology with a single hidden layer of 3 to 16 neurons and 1 to 4 input delays is used. The training performance is measured by averaged mean square error. It is found that the training performance cannot be improved by solely increasing the training data size. However, the training performance can be improved by increasing the precision of the training data. This provides useful knowledge towards reducing the number of EEG data samples and corresponding acquisition time for prediction.
{"title":"Evaluating the Effects of Size and Precision of Training Data on ANN Training Performance for the Prediction of Chaotic Time Series Patterns","authors":"Lei Zhang","doi":"10.4018/IJSSCI.2019010102","DOIUrl":"https://doi.org/10.4018/IJSSCI.2019010102","url":null,"abstract":"In this research, artificial neural networks (ANN) with various architectures are trained to generate the chaotic time series patterns of the Lorenz attractor. The ANN training performance is evaluated based on the size and precision of the training data. The nonlinear Auto-Regressive (NAR) model is trained in open loop mode first. The trained model is then used with closed loop feedback to predict the chaotic time series outputs. The research goal is to use the designed NAR ANN model for the simulation and analysis of Electroencephalogram (EEG) signals in order to study brain activities. A simple ANN topology with a single hidden layer of 3 to 16 neurons and 1 to 4 input delays is used. The training performance is measured by averaged mean square error. It is found that the training performance cannot be improved by solely increasing the training data size. However, the training performance can be improved by increasing the precision of the training data. This provides useful knowledge towards reducing the number of EEG data samples and corresponding acquisition time for prediction.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130340719","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 : 2019-01-01DOI: 10.4018/IJSSCI.2019010104
M. Hussain, M. Beg
The advent of intelligent vehicular applications and IoT technologies gives rise to data-intensive challenges across different architectural layers of an intelligent transportation system (ITS). Without powerful communication and computational infrastructure, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in daily life. The current cloud computing and cellular set-ups are far from perfect because they are highly dependent on, and bear the cost of additional infrastructure deployment. Thus, the geo-distributed ITS components require a paradigm shift from centralized cloud-scale processing to edge centered fog computing (FC) paradigms. FC outspreads the computing facilities into the edge of a network, offering location-awareness, latency-sensitive monitoring, and intelligent control. In this article, the authors identify the mission-critical computing needs of the next generation ITS applications and highlight the scopes of FC based solutions towards addressing them. Then, the authors discuss the scenarios where the underutilized communication and computational resources available in connected vehicles can be brought in to perform the role of FC infrastructures. Then the authors present a service-oriented software architecture (SOA) for FC-based Big Data Analytics in ITS applications. The authors also provide a detailed analysis of the potential challenges of using connected vehicles as FC infrastructures along with future research directions.
{"title":"Using Vehicles as Fog Infrastructures for Transportation Cyber-Physical Systems (T-CPS): Fog Computing for Vehicular Networks","authors":"M. Hussain, M. Beg","doi":"10.4018/IJSSCI.2019010104","DOIUrl":"https://doi.org/10.4018/IJSSCI.2019010104","url":null,"abstract":"The advent of intelligent vehicular applications and IoT technologies gives rise to data-intensive challenges across different architectural layers of an intelligent transportation system (ITS). Without powerful communication and computational infrastructure, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in daily life. The current cloud computing and cellular set-ups are far from perfect because they are highly dependent on, and bear the cost of additional infrastructure deployment. Thus, the geo-distributed ITS components require a paradigm shift from centralized cloud-scale processing to edge centered fog computing (FC) paradigms. FC outspreads the computing facilities into the edge of a network, offering location-awareness, latency-sensitive monitoring, and intelligent control. In this article, the authors identify the mission-critical computing needs of the next generation ITS applications and highlight the scopes of FC based solutions towards addressing them. Then, the authors discuss the scenarios where the underutilized communication and computational resources available in connected vehicles can be brought in to perform the role of FC infrastructures. Then the authors present a service-oriented software architecture (SOA) for FC-based Big Data Analytics in ITS applications. The authors also provide a detailed analysis of the potential challenges of using connected vehicles as FC infrastructures along with future research directions.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114075422","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 : 2019-01-01DOI: 10.4018/IJSSCI.2019010103
A. Pandey, S. Banerjee
Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.
{"title":"Test Suite Optimization Using Firefly and Genetic Algorithm","authors":"A. Pandey, S. Banerjee","doi":"10.4018/IJSSCI.2019010103","DOIUrl":"https://doi.org/10.4018/IJSSCI.2019010103","url":null,"abstract":"Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116413242","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 : 2018-10-01DOI: 10.4018/IJSSCI.2018100104
Sandhya Chengaiyan, Divya Balathayil, K. Anandan, T. Bobby
Speech imagery is one form of mental imagery which refers to the imagining of speaking a word to oneself silently in the mind without any articulation movement. In this work, electroencephalography (EEG) signals were acquired while speaking and during the imagining of speaking consonant-vowel-consonant (CVC) words in multiple trials of different time frames. Relative powers were computed for each EEG frequency band. It has been observed that relative power of alpha and theta bands was dominant. Phase Locking Value (PLV), a functional brain connectivity parameter has been estimated to understand the phase synchronicity between two brain regions. PLV results show that the left hemispheric frontal and temporal electrodes has maximum phase lock in alpha and theta band during speech and speech imagery process. The combination of brain connectivity estimators and signal processing techniques will thus be a reliable framework for understanding the nature of speech imagery signals captured through EEG.
{"title":"Effect of Power and Phase Synchronization in Multi-Trial Speech Imagery","authors":"Sandhya Chengaiyan, Divya Balathayil, K. Anandan, T. Bobby","doi":"10.4018/IJSSCI.2018100104","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018100104","url":null,"abstract":"Speech imagery is one form of mental imagery which refers to the imagining of speaking a word to oneself silently in the mind without any articulation movement. In this work, electroencephalography (EEG) signals were acquired while speaking and during the imagining of speaking consonant-vowel-consonant (CVC) words in multiple trials of different time frames. Relative powers were computed for each EEG frequency band. It has been observed that relative power of alpha and theta bands was dominant. Phase Locking Value (PLV), a functional brain connectivity parameter has been estimated to understand the phase synchronicity between two brain regions. PLV results show that the left hemispheric frontal and temporal electrodes has maximum phase lock in alpha and theta band during speech and speech imagery process. The combination of brain connectivity estimators and signal processing techniques will thus be a reliable framework for understanding the nature of speech imagery signals captured through EEG.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125336163","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 : 2018-10-01DOI: 10.4018/IJSSCI.2018100103
Hongbo Wang, Qingdong Su, Ruolei Zeng
In recent years, the quadratic force-directed placement is becoming popular due to its stable quality at low power. The force-directed placement composes of two operations, namely, orientating and modulating. The two actions are going on until the overlap degree can meet a predetermined target. Different methods have a great influence on their quality of a layout. A novel encoding strategy of two-dimensional chromosome based on immune cooperative optimization is suggested. The main works first focus on a multi-point crossover strategy, and its Poisson distribution makes use of a Euclidean distance density between the concentration of antibody suppression and the translation variation of optimal gene pairs in two-dimension. Then, a flexible region division is proposed for dealing with the layout problem of gate array. The related experiment indicates the constructed encoding strategy for gate array placement is effective and efficient.
{"title":"Cooperative Encoding Strategy for Gate Array Placement","authors":"Hongbo Wang, Qingdong Su, Ruolei Zeng","doi":"10.4018/IJSSCI.2018100103","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018100103","url":null,"abstract":"In recent years, the quadratic force-directed placement is becoming popular due to its stable quality at low power. The force-directed placement composes of two operations, namely, orientating and modulating. The two actions are going on until the overlap degree can meet a predetermined target. Different methods have a great influence on their quality of a layout. A novel encoding strategy of two-dimensional chromosome based on immune cooperative optimization is suggested. The main works first focus on a multi-point crossover strategy, and its Poisson distribution makes use of a Euclidean distance density between the concentration of antibody suppression and the translation variation of optimal gene pairs in two-dimension. Then, a flexible region division is proposed for dealing with the layout problem of gate array. The related experiment indicates the constructed encoding strategy for gate array placement is effective and efficient.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130764288","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 : 2018-10-01DOI: 10.4018/IJSSCI.2018100102
A. D. Gavrilov, Alex Jordache, Maya Vasdani, Jack Deng
The current discourse in the machine learning domain converges to the agreement that machine learning methods emerged as some of the most prominent learning and classification approaches over the past decade. The CNN became one of most actively researched and broadly-applied deep machine learning methods. However, the training set has a large influence on the accuracy of a network and it is paramount to create an architecture that supports its maximum training and recognition performance. The problem considered in this article is how to prevent overfitting and underfitting. The deficiencies are addressed by comparing the statistics of CNN image recognition algorithms to the Ising model. Using a two-dimensional square-lattice array, the impact that the learning rate and regularization rate parameters have on the adaptability of CNNs for image classification are evaluated. The obtained results contribute to a better theoretical understanding of a CNN and provide concrete guidance on preventing model overfitting and underfitting when a CNN is applied for image recognition tasks.
{"title":"Preventing Model Overfitting and Underfitting in Convolutional Neural Networks","authors":"A. D. Gavrilov, Alex Jordache, Maya Vasdani, Jack Deng","doi":"10.4018/IJSSCI.2018100102","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018100102","url":null,"abstract":"The current discourse in the machine learning domain converges to the agreement that machine learning methods emerged as some of the most prominent learning and classification approaches over the past decade. The CNN became one of most actively researched and broadly-applied deep machine learning methods. However, the training set has a large influence on the accuracy of a network and it is paramount to create an architecture that supports its maximum training and recognition performance. The problem considered in this article is how to prevent overfitting and underfitting. The deficiencies are addressed by comparing the statistics of CNN image recognition algorithms to the Ising model. Using a two-dimensional square-lattice array, the impact that the learning rate and regularization rate parameters have on the adaptability of CNNs for image classification are evaluated. The obtained results contribute to a better theoretical understanding of a CNN and provide concrete guidance on preventing model overfitting and underfitting when a CNN is applied for image recognition tasks.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124664122","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 : 2018-10-01DOI: 10.4018/IJSSCI.2018100101
Jila Hosseinkhani, C. Joslin
A key factor in designing saliency detection algorithms for videos is to understand how different visual cues affect the human perceptual and visual system. To this end, this article investigated the bottom-up features including color, texture, and motion in video sequences for a one-by-one scenario to provide a ranking system stating the most dominant circumstances for each feature. In this work, it is considered the individual features and various visual saliency attributes investigated under conditions in which the authors had no cognitive bias. Human cognition refers to a systematic pattern of perceptual and rational judgments and decision-making actions. First, this paper modeled the test data as 2D videos in a virtual environment to avoid any cognitive bias. Then, this paper performed an experiment using human subjects to determine which colors, textures, motion directions, and motion speeds attract human attention more. The proposed benchmark ranking system of salient visual attention stimuli was achieved using an eye tracking procedure.
{"title":"Saliency Priority of Individual Bottom-Up Attributes in Designing Visual Attention Models","authors":"Jila Hosseinkhani, C. Joslin","doi":"10.4018/IJSSCI.2018100101","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018100101","url":null,"abstract":"A key factor in designing saliency detection algorithms for videos is to understand how different visual cues affect the human perceptual and visual system. To this end, this article investigated the bottom-up features including color, texture, and motion in video sequences for a one-by-one scenario to provide a ranking system stating the most dominant circumstances for each feature. In this work, it is considered the individual features and various visual saliency attributes investigated under conditions in which the authors had no cognitive bias. Human cognition refers to a systematic pattern of perceptual and rational judgments and decision-making actions. First, this paper modeled the test data as 2D videos in a virtual environment to avoid any cognitive bias. Then, this paper performed an experiment using human subjects to determine which colors, textures, motion directions, and motion speeds attract human attention more. The proposed benchmark ranking system of salient visual attention stimuli was achieved using an eye tracking procedure.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129971235","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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