Pub Date : 2019-06-01DOI: 10.1109/iacs.2019.8809162
{"title":"[ICICS 2019 Title page]","authors":"","doi":"10.1109/iacs.2019.8809162","DOIUrl":"https://doi.org/10.1109/iacs.2019.8809162","url":null,"abstract":"","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122210085","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 : 2019-06-01DOI: 10.1109/IACS.2019.8809117
Omar Almomani, Adeeb Saaidah, Firas Al Balas, L. Al-Qaisi
Congestion crumbles the network performance, therefore. This paper evaluates several Active Queue Management (AQM) algorithms which used to control congestion. The main Objective of the paper is to find optimal algorithm that can be use to avoid congestion. AQM are router based mechanism which can detect congestion in early stage in the network ask the transmitter to decrease its transmitting rate, in this way the network can control the congestion for incoming packets. So some of AQM algorithms were evaluated by analyzed their performance, the selected AQM algorithms are Gentle BLUE (GB), Dynamic Gentle Random Early Detection (DGRED), Effective Random Early Detection (ERED), BLUE and Adaptive Max Threshold algorithms. Performance evaluation is carried by using JAVA simulation environments. Evaluation results show that GB compared with DGRED, ERED, BLUE and Adaptive Max Threshold outperformed in terms of mean queue length, delay and packet loss. GB had maximum dropping probability as compare with DGRED, ERED, BLUE and Adaptive Max Threshold. In term of throughput all tested algorithms all most give same throughput. The results prove that the GB is can be appropriate algorithm to handle congestion as compare to DGRED, ERED, BLUE and Adaptive Max Threshold.
因此，拥塞会降低网络性能。本文评价了几种用于控制拥塞的主动队列管理(AQM)算法。本文的主要目标是找到可用于避免拥塞的最优算法。AQM是一种基于路由器的机制，它可以在网络的早期检测到拥塞，要求发送方降低传输速率，从而控制传入数据包的拥塞。本文通过对几种AQM算法的性能分析，对几种AQM算法进行了评价，选取的AQM算法有:Gentle BLUE (GB)算法、Dynamic Gentle Random Early Detection (DGRED)算法、Effective Random Early Detection (ERED)算法、BLUE算法和Adaptive Max Threshold算法。利用JAVA仿真环境进行性能评估。评估结果表明，与DGRED、ERED、BLUE和Adaptive Max Threshold算法相比，GB算法在平均队列长度、延迟和丢包方面表现优于DGRED算法。与DGRED、ERED、BLUE和Adaptive Max Threshold相比，GB具有最大的掉落概率。在吞吐量方面，所有被测试的算法都给出了相同的吞吐量。结果表明，与DGRED、ERED、BLUE和Adaptive Max Threshold算法相比，GB is算法是一种较好的拥塞处理算法。
{"title":"Simulation Based Performance Evaluation of Several Active Queue Management Algorithms for Computer Network","authors":"Omar Almomani, Adeeb Saaidah, Firas Al Balas, L. Al-Qaisi","doi":"10.1109/IACS.2019.8809117","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809117","url":null,"abstract":"Congestion crumbles the network performance, therefore. This paper evaluates several Active Queue Management (AQM) algorithms which used to control congestion. The main Objective of the paper is to find optimal algorithm that can be use to avoid congestion. AQM are router based mechanism which can detect congestion in early stage in the network ask the transmitter to decrease its transmitting rate, in this way the network can control the congestion for incoming packets. So some of AQM algorithms were evaluated by analyzed their performance, the selected AQM algorithms are Gentle BLUE (GB), Dynamic Gentle Random Early Detection (DGRED), Effective Random Early Detection (ERED), BLUE and Adaptive Max Threshold algorithms. Performance evaluation is carried by using JAVA simulation environments. Evaluation results show that GB compared with DGRED, ERED, BLUE and Adaptive Max Threshold outperformed in terms of mean queue length, delay and packet loss. GB had maximum dropping probability as compare with DGRED, ERED, BLUE and Adaptive Max Threshold. In term of throughput all tested algorithms all most give same throughput. The results prove that the GB is can be appropriate algorithm to handle congestion as compare to DGRED, ERED, BLUE and Adaptive Max Threshold.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115217732","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 : 2019-06-01DOI: 10.1109/IACS.2019.8809125
Qasem Abu Al-Haija, L. Tawalbeh
Recently, cybercrimes are causing huge impact on different cyber systems that might include vital information such as financial transactions and medical records. A better understanding of the accelerating numbers of cybercrimes and their enormous cost could help the global in bridging the gap between their defenses and the escalating numbers cyber criminals. In this paper, we present an estimation model of cybercrimes time series using auto-regressive (AR) model by employing the optimal modeling order that maximizes the estimation accuracy while maintaining minimum prediction error. The proposed model was developed using Matlab to estimate the time series for yearly global number of cybersecurity incidents activity during the period from 2009-2018 and forecast the figures for next upcoming years 2019-2020. The simulation results showed that the optimal model order to estimate the given cybercrime activity is AR(4) since its corresponds to minimum acceptable predication error values to estimate the signal recording an estimation accuracy of 93.5%.
{"title":"Autoregressive Modeling and Prediction of Annual Worldwide Cybercrimes for Cloud Environments","authors":"Qasem Abu Al-Haija, L. Tawalbeh","doi":"10.1109/IACS.2019.8809125","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809125","url":null,"abstract":"Recently, cybercrimes are causing huge impact on different cyber systems that might include vital information such as financial transactions and medical records. A better understanding of the accelerating numbers of cybercrimes and their enormous cost could help the global in bridging the gap between their defenses and the escalating numbers cyber criminals. In this paper, we present an estimation model of cybercrimes time series using auto-regressive (AR) model by employing the optimal modeling order that maximizes the estimation accuracy while maintaining minimum prediction error. The proposed model was developed using Matlab to estimate the time series for yearly global number of cybersecurity incidents activity during the period from 2009-2018 and forecast the figures for next upcoming years 2019-2020. The simulation results showed that the optimal model order to estimate the given cybercrime activity is AR(4) since its corresponds to minimum acceptable predication error values to estimate the signal recording an estimation accuracy of 93.5%.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114146802","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 : 2019-06-01DOI: 10.1109/IACS.2019.8809114
Maad Ebrahim, M. Al-Ayyoub, M. Alsmirat
The huge impact of Transfer Learning (TL) techniques in many fields was achieved using several state-of-the-art ImageNet-pretrained models. These models have shown great performance improvements on this dataset over the last few years. One of the recently used TL techniques is feature extraction with the help of Feature Concatenation (FC), where the extracted features of multiple pretrained models are concatenated together before training on them, to produce more robust and discriminative feature representations on various classification tasks. However, neither TL nor FC techniques have been tested on the same dataset that initially trained the pretrained models, i.e. ImageNet. Hence, this work provides an investigative study to test the possibility of improving the ImageNet accuracy using the feature extraction approach of TL with the help of FC techniques. The results of this work show that there is no TL technique that can be used with or without FC to increase the accuracy of pretrained models on the original dataset on which they were trained. Even for FC, it cannot produce a more discriminative feature representation for the original data than what the individual models can produce.
{"title":"Will Transfer Learning Enhance ImageNet Classification Accuracy Using ImageNet-Pretrained Models?","authors":"Maad Ebrahim, M. Al-Ayyoub, M. Alsmirat","doi":"10.1109/IACS.2019.8809114","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809114","url":null,"abstract":"The huge impact of Transfer Learning (TL) techniques in many fields was achieved using several state-of-the-art ImageNet-pretrained models. These models have shown great performance improvements on this dataset over the last few years. One of the recently used TL techniques is feature extraction with the help of Feature Concatenation (FC), where the extracted features of multiple pretrained models are concatenated together before training on them, to produce more robust and discriminative feature representations on various classification tasks. However, neither TL nor FC techniques have been tested on the same dataset that initially trained the pretrained models, i.e. ImageNet. Hence, this work provides an investigative study to test the possibility of improving the ImageNet accuracy using the feature extraction approach of TL with the help of FC techniques. The results of this work show that there is no TL technique that can be used with or without FC to increase the accuracy of pretrained models on the original dataset on which they were trained. Even for FC, it cannot produce a more discriminative feature representation for the original data than what the individual models can produce.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126132599","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 : 2019-06-01DOI: 10.1109/IACS.2019.8809178
G. Saldamli, Himanshu Mishra, Nikhil Ravi, Rahul Rao Kodati, Sahithi A. Kuntamukkala, L. Tawalbeh
Software-Defined Networking is considered as a prominent architecture in the field of networking. The separation of control plane with data plane provides an efficient way to implement and manage real-world networks. In recent years, there has been a major boost in network traffic due to the advancement in IoT and cloud technologies. The main drive behind the SDNs is primarily due to the fact that existing legacy networks do not have the capacity to manage such large volumes of traffic in coming future. One of the major concern related to the implementation of SDN networks is the recovery time in case of link failure and congestion. This is known to be the main bottleneck for the scalability of pure SDN networks. The existing method in use requires the SDN devices to contact the centralized controller to act during such events. However, in real-world networks consisting of many devices, such a solution increases the latency immensely thus reducing the efficiency of the network. In here, we propose a method to improve the recovery time for link failure and congestion events. The method consists of controller modules that proactively install rules before the occurrence of such events. This will provide the ability to these devices to handle such issues at the data plane without the intervention of controller. The rules are later pushed to SDN devices at regular intervals using different algorithms and RESTful methods. The proposed solution largely improves the efficiency of these networks by reducing their overall latency.
{"title":"Improving link failure recovery and congestion control in SDNs","authors":"G. Saldamli, Himanshu Mishra, Nikhil Ravi, Rahul Rao Kodati, Sahithi A. Kuntamukkala, L. Tawalbeh","doi":"10.1109/IACS.2019.8809178","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809178","url":null,"abstract":"Software-Defined Networking is considered as a prominent architecture in the field of networking. The separation of control plane with data plane provides an efficient way to implement and manage real-world networks. In recent years, there has been a major boost in network traffic due to the advancement in IoT and cloud technologies. The main drive behind the SDNs is primarily due to the fact that existing legacy networks do not have the capacity to manage such large volumes of traffic in coming future. One of the major concern related to the implementation of SDN networks is the recovery time in case of link failure and congestion. This is known to be the main bottleneck for the scalability of pure SDN networks. The existing method in use requires the SDN devices to contact the centralized controller to act during such events. However, in real-world networks consisting of many devices, such a solution increases the latency immensely thus reducing the efficiency of the network. In here, we propose a method to improve the recovery time for link failure and congestion events. The method consists of controller modules that proactively install rules before the occurrence of such events. This will provide the ability to these devices to handle such issues at the data plane without the intervention of controller. The rules are later pushed to SDN devices at regular intervals using different algorithms and RESTful methods. The proposed solution largely improves the efficiency of these networks by reducing their overall latency.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129139230","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 : 2019-06-01DOI: 10.1109/iacs.2019.8809177
{"title":"ICICS 2019 Committees","authors":"","doi":"10.1109/iacs.2019.8809177","DOIUrl":"https://doi.org/10.1109/iacs.2019.8809177","url":null,"abstract":"","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114552558","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 : 2019-06-01DOI: 10.1109/IACS.2019.8809161
Lamia Fatiha Kazi Tani, Abdelghani Ghomari, Mohammed Yassine Kazi Tani
Currently, the video annotation process becomes a very necessary task to overcome the problem of finding the adequate information on a huge database. To achieve this, it will be necessary to translate low level analysis results to a semantic meaning related to the video sequence, generally known as problem of semantic gap. Many researchers have worked on this issue in the aim to find new solutions, where the ontology paradigm is represented as one of them. In this paper, we consider this problem by developing a soccer ontology, that is used in our OSAS annotation system (Ontology Soccer Annotation System). This one is based on a set of Semantic Web Rule Language (SWRL) rules that bridges the semantic gap problem. Our idea show new directions and perspectives for future developments.
{"title":"A Semi-Automatic Soccer Video Annotation System based on Ontology Paradigm","authors":"Lamia Fatiha Kazi Tani, Abdelghani Ghomari, Mohammed Yassine Kazi Tani","doi":"10.1109/IACS.2019.8809161","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809161","url":null,"abstract":"Currently, the video annotation process becomes a very necessary task to overcome the problem of finding the adequate information on a huge database. To achieve this, it will be necessary to translate low level analysis results to a semantic meaning related to the video sequence, generally known as problem of semantic gap. Many researchers have worked on this issue in the aim to find new solutions, where the ontology paradigm is represented as one of them. In this paper, we consider this problem by developing a soccer ontology, that is used in our OSAS annotation system (Ontology Soccer Annotation System). This one is based on a set of Semantic Web Rule Language (SWRL) rules that bridges the semantic gap problem. Our idea show new directions and perspectives for future developments.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127005316","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 : 2019-06-01DOI: 10.1109/IACS.2019.8809163
Hussam Awwad
This paper introduces suggestion to reduce the losses and voltage drop by reactive power compensation and cable sizing. This losses reduction ratio, the annual saving and reducing in voltage drop is a good motivation to rehabilitate the Gaza Governorate Electrical Grid by applying this suggestion. The grid was unbalanced in most cases, there was a big difference in losses and voltage drop between balance and unbalanced load in the two feeders have been taken as a case study.
{"title":"Applied Study of Energy Saving, Voltage Drop Reducing Technically Using Reactive Power Compensation and Cable Resizing in Gaza Electrical Grid and its Program Simulation Quality Improvement","authors":"Hussam Awwad","doi":"10.1109/IACS.2019.8809163","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809163","url":null,"abstract":"This paper introduces suggestion to reduce the losses and voltage drop by reactive power compensation and cable sizing. This losses reduction ratio, the annual saving and reducing in voltage drop is a good motivation to rehabilitate the Gaza Governorate Electrical Grid by applying this suggestion. The grid was unbalanced in most cases, there was a big difference in losses and voltage drop between balance and unbalanced load in the two feeders have been taken as a case study.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123763688","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 : 2019-06-01DOI: 10.1109/IACS.2019.8809133
Fatima Koumi, M. Aldasht, H. Tamimi
In machine learning, feature selection can be used to reduce the computation time and improve the learning accuracy, especially when dealing with high-dimensional data sets. Particle Swarm Optimization (PSO) has attracted significant concerns to enhance the feature selection process due to its efficiency in solving problems. This paper introduces a new hybrid filters-wrapper approach that is used to enhance the feature selection process using PSO algorithm. Our proposed approach combines five filtration methods in with different weights to produce a new hybrid filters-wrapper algorithm using BPSO. The proposed approach has been evaluated by performing comparisons with other methods like wrapper alone and filter alone. The obtained results show that the proposed approach has achieved better performance than other approaches taking into account three parameters; The number of selected features, the classification accuracy, and the execution time. In addition, the new approach has been tested to ensure its stability in the feature selection and it has shown a high degree of stability.
{"title":"Efficient Feature Selection using Particle Swarm Optimization: A hybrid filters-wrapper Approach","authors":"Fatima Koumi, M. Aldasht, H. Tamimi","doi":"10.1109/IACS.2019.8809133","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809133","url":null,"abstract":"In machine learning, feature selection can be used to reduce the computation time and improve the learning accuracy, especially when dealing with high-dimensional data sets. Particle Swarm Optimization (PSO) has attracted significant concerns to enhance the feature selection process due to its efficiency in solving problems. This paper introduces a new hybrid filters-wrapper approach that is used to enhance the feature selection process using PSO algorithm. Our proposed approach combines five filtration methods in with different weights to produce a new hybrid filters-wrapper algorithm using BPSO. The proposed approach has been evaluated by performing comparisons with other methods like wrapper alone and filter alone. The obtained results show that the proposed approach has achieved better performance than other approaches taking into account three parameters; The number of selected features, the classification accuracy, and the execution time. In addition, the new approach has been tested to ensure its stability in the feature selection and it has shown a high degree of stability.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132318058","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 : 2019-06-01DOI: 10.1109/IACS.2019.8809137
Mohammad Al Saaideh, Bayan B. Mazideh, D. Abu-Al-Nadi
Digital filters play an important role in digital signal processing applications. In this work Grey Wolf Optimizer (GWO) algorithm is used to design optimal digital UR filters. The multiobjective function based on minimizing the absolute error (L1-norm), the square of error (L2-norm) and the ripple of magnitude of passband and stopband frequencies is used to solve the optimization problem with stability constraints. The simulation results of designed Low-pass (LP), High-pass (HP), Band-pass (BP) and Band-stop (BS) show the ability and success of using GWO for design digital HR filters and it gives frequency response with small absolute error and design specifications within suitable range as compared with other algorithms in literature.
{"title":"Grey Wolf Optimizer for Optimal Design of Digital HR Filter","authors":"Mohammad Al Saaideh, Bayan B. Mazideh, D. Abu-Al-Nadi","doi":"10.1109/IACS.2019.8809137","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809137","url":null,"abstract":"Digital filters play an important role in digital signal processing applications. In this work Grey Wolf Optimizer (GWO) algorithm is used to design optimal digital UR filters. The multiobjective function based on minimizing the absolute error (L1-norm), the square of error (L2-norm) and the ripple of magnitude of passband and stopband frequencies is used to solve the optimization problem with stability constraints. The simulation results of designed Low-pass (LP), High-pass (HP), Band-pass (BP) and Band-stop (BS) show the ability and success of using GWO for design digital HR filters and it gives frequency response with small absolute error and design specifications within suitable range as compared with other algorithms in literature.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125566642","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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