Tamara Stajić, J. Jovanović, Nebojša Jovanović, M. Janković
Recognizing and accurately classifying human emotion is a complex and challenging task. Recently, great attention has been paid to the emotion recognition methods using three different approaches: based on non-physiological signals (like speech and facial expression), based on physiological signals, or based on hybrid approaches. Non-physiological signals are easily controlled by the individual, so these approaches have downsides in real world applications. In this paper, an approach based on physiological signals which cannot be willingly influenced (electroencephalogram, heartrate, respiration, galvanic skin response, electromyography, body temperature) is presented. A publicly available DEAP database was used for the binary classification (high vs low for various threshold values) considering four frequently used emotional parameters (arousal, valence, liking and dominance). We have extracted 1490 features from the dataset, analyzed their predictive value for each emotion parameter and compared three different classification approaches - Support Vector Machine, Boosting algorithms and Artificial Neural Networks.
{"title":"Comparison of machine learning approaches to emotion recognition based on deap database physiological signals","authors":"Tamara Stajić, J. Jovanović, Nebojša Jovanović, M. Janković","doi":"10.5937/telfor2202073s","DOIUrl":"https://doi.org/10.5937/telfor2202073s","url":null,"abstract":"Recognizing and accurately classifying human emotion is a complex and challenging task. Recently, great attention has been paid to the emotion recognition methods using three different approaches: based on non-physiological signals (like speech and facial expression), based on physiological signals, or based on hybrid approaches. Non-physiological signals are easily controlled by the individual, so these approaches have downsides in real world applications. In this paper, an approach based on physiological signals which cannot be willingly influenced (electroencephalogram, heartrate, respiration, galvanic skin response, electromyography, body temperature) is presented. A publicly available DEAP database was used for the binary classification (high vs low for various threshold values) considering four frequently used emotional parameters (arousal, valence, liking and dominance). We have extracted 1490 features from the dataset, analyzed their predictive value for each emotion parameter and compared three different classification approaches - Support Vector Machine, Boosting algorithms and Artificial Neural Networks.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71141404","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}
This paper presents a software prototype of a wireless network for the Internet of Things (IoT) based on the DECT (Digital Enhanced Cordless Telecommunication) standard. It proposes an architecture for encapsulating commands from the most common IoT protocol, MQTT (Message Queuing Telemetry Transport), into SIP (Session Initiation Protocol) packets. A module is created to embed MQTT-SN (MQTT for Sensor Networks) packets into SIP packets. The module is developed in Go language using the built-in "net" library. Delivery of MQTT-SN packets to IoT devices is carried out using the SIP protocol. Source codes and instructions for installing the gateway can be found at https://github.com/iSinyavsky/mqtt-sn-sip-gateway.
{"title":"Prototype wireless network for internet of things based on DECT standard","authors":"Ivan V. Sinyavskiy, Igor M. Sorokin, A. Sukhov","doi":"10.5937/telfor2201008s","DOIUrl":"https://doi.org/10.5937/telfor2201008s","url":null,"abstract":"This paper presents a software prototype of a wireless network for the Internet of Things (IoT) based on the DECT (Digital Enhanced Cordless Telecommunication) standard. It proposes an architecture for encapsulating commands from the most common IoT protocol, MQTT (Message Queuing Telemetry Transport), into SIP (Session Initiation Protocol) packets. A module is created to embed MQTT-SN (MQTT for Sensor Networks) packets into SIP packets. The module is developed in Go language using the built-in \"net\" library. Delivery of MQTT-SN packets to IoT devices is carried out using the SIP protocol. Source codes and instructions for installing the gateway can be found at https://github.com/iSinyavsky/mqtt-sn-sip-gateway.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71140697","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}
In this work, we propose a software library written in C for encoding and decoding Reed-Solomon codes. Library consists of one scalar CODEC and two vectorized codecs for x86 architecture. Vectorized codecs use the benefits of SSSE3 or AVX2 instruction sets. We compare the performance of our three codecs with the JPWL RS CODEC from the Open JPEG library. The performance comparison methodology is described, and it is based on the measuring of the encoding and decoding speed. The results demonstrate a 4.1x speed gain with the scalar CODEC and a 19.6x gain with the vectorized CODEC. Based on testing results and supported instruction sets, a dynamic selection of CODEC version is proposed.
{"title":"Software optimization for fast encoding and decoding of Reed-Solomon codes","authors":"Sergey Skorokhod, Andrey Barlit","doi":"10.5937/telfor2202056s","DOIUrl":"https://doi.org/10.5937/telfor2202056s","url":null,"abstract":"In this work, we propose a software library written in C for encoding and decoding Reed-Solomon codes. Library consists of one scalar CODEC and two vectorized codecs for x86 architecture. Vectorized codecs use the benefits of SSSE3 or AVX2 instruction sets. We compare the performance of our three codecs with the JPWL RS CODEC from the Open JPEG library. The performance comparison methodology is described, and it is based on the measuring of the encoding and decoding speed. The results demonstrate a 4.1x speed gain with the scalar CODEC and a 19.6x gain with the vectorized CODEC. Based on testing results and supported instruction sets, a dynamic selection of CODEC version is proposed.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71141023","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}
Veličko Krsmanović, M. Barjaktarović, A. Gavrovska
In this paper a new system for 3D (three-dimensional) mapping using affordable LIDAR (light detection and ranging) is presented. The implementation of LIDAR technology-based approach enables obtaining a point cloud as a representation of indoor surrounding. In recent years with the help of LIDAR this kind of sensing has found numerous applications across various industries. Here, a cloud of points is generated during room scanning using Arduino platform based rotating system. The obtained results are promising, and the proposed solution can find its practical application in different fields. Moreover, it can provide many possibilities for future experiments with surrounding mappings, image matching, autonomous driving, obstacle observation, collision avoidance, material type detection such as transparent ones.
{"title":"System for 3D mapping using affordable LIDAR","authors":"Veličko Krsmanović, M. Barjaktarović, A. Gavrovska","doi":"10.5937/telfor2202067k","DOIUrl":"https://doi.org/10.5937/telfor2202067k","url":null,"abstract":"In this paper a new system for 3D (three-dimensional) mapping using affordable LIDAR (light detection and ranging) is presented. The implementation of LIDAR technology-based approach enables obtaining a point cloud as a representation of indoor surrounding. In recent years with the help of LIDAR this kind of sensing has found numerous applications across various industries. Here, a cloud of points is generated during room scanning using Arduino platform based rotating system. The obtained results are promising, and the proposed solution can find its practical application in different fields. Moreover, it can provide many possibilities for future experiments with surrounding mappings, image matching, autonomous driving, obstacle observation, collision avoidance, material type detection such as transparent ones.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71141373","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}
In this paper, we analyze applicability of single-and two-hidden-layer feed-forward artificial neural networks, SLFNs and TLFNs, respectively, in decoding linear block codes. Based on the provable capability of SLFNs and TLFNs to approximate discrete functions, we discuss sizes of the network capable to perform maximum likelihood decoding. Furthermore, we propose a decoding scheme, which use artificial neural networks (ANNs) to lower the error-floors of low-density parity-check (LDPC) codes. By learning a small number of error patterns, uncorrectable with typical decoders of LDPC codes, ANN can lower the error-floor by an order of magnitude, with only marginal average complexity incense.
{"title":"On guaranteed correction of error patterns with artificial neural networks","authors":"Srdan Brkic, P. Ivaniš, B. Vasic","doi":"10.5937/telfor2202051b","DOIUrl":"https://doi.org/10.5937/telfor2202051b","url":null,"abstract":"In this paper, we analyze applicability of single-and two-hidden-layer feed-forward artificial neural networks, SLFNs and TLFNs, respectively, in decoding linear block codes. Based on the provable capability of SLFNs and TLFNs to approximate discrete functions, we discuss sizes of the network capable to perform maximum likelihood decoding. Furthermore, we propose a decoding scheme, which use artificial neural networks (ANNs) to lower the error-floors of low-density parity-check (LDPC) codes. By learning a small number of error patterns, uncorrectable with typical decoders of LDPC codes, ANN can lower the error-floor by an order of magnitude, with only marginal average complexity incense.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71140809","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}
Yanti Andriyani, Al Aminuddin, Evfi Mahdiyah, N. Ario
Designing a system is an important step in the software development process. A use case diagram (UCD) and a class diagram (CD) are the most used diagrams in designing a system. This case study aims to explore the implementation of an event-based approach using the event table (ET) to design the Outcome-based Education Curriculum System (OBECS). In generating a UCD of OBECS, the event-based approach involves three processes: (1) identifying actors and the relationship between actors; (2) identifying use cases and the relationships of the use cases; and (3) generating UCD. Meanwhile, there are four processes in the event-based approach which can be used to generate a CD of OBECS namely: (1) identifying the classes for each event or action using the ET;(2) identifying the relationships between sources and objects; (3) identifying the class' attributes and methods; and (4) integrating all classes. Our study proposes a clear and simple concept to generate a UCD and CD in designing a system. It is expected that the result of the current study could help a software designer in modelling the system from the system requirement.
{"title":"Event-based approach for analyzing and designing system: A case study of designing curriculum system","authors":"Yanti Andriyani, Al Aminuddin, Evfi Mahdiyah, N. Ario","doi":"10.5937/telfor2201033a","DOIUrl":"https://doi.org/10.5937/telfor2201033a","url":null,"abstract":"Designing a system is an important step in the software development process. A use case diagram (UCD) and a class diagram (CD) are the most used diagrams in designing a system. This case study aims to explore the implementation of an event-based approach using the event table (ET) to design the Outcome-based Education Curriculum System (OBECS). In generating a UCD of OBECS, the event-based approach involves three processes: (1) identifying actors and the relationship between actors; (2) identifying use cases and the relationships of the use cases; and (3) generating UCD. Meanwhile, there are four processes in the event-based approach which can be used to generate a CD of OBECS namely: (1) identifying the classes for each event or action using the ET;(2) identifying the relationships between sources and objects; (3) identifying the class' attributes and methods; and (4) integrating all classes. Our study proposes a clear and simple concept to generate a UCD and CD in designing a system. It is expected that the result of the current study could help a software designer in modelling the system from the system requirement.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71140674","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}
In this paper, we apply Deep Learning (DL) and decision-tree-based ensemble learning algorithms to classify network traffic by application. Various Deep Learning (DL) models for network traffic identification have been presented, implemented and compared, including 1D convolutional, stacked autoencoder, multi-layer perceptron, and combination of the aforementioned. Then the results of DL models have been compared to those obtained with two popular ensemble learning models based on decision trees-Random Forest and XGBoost. To train and test the classification models, a dataset containing both encrypted and unencrypted traffic has been collected in a real network, under normal operating conditions, and pre-processed in a way that ensures non-biased results. The classification uncertainties of the models have been also quantified on publicly available ISCX VPN-nonVPN dataset. The models have been compared in terms of precision, recall, F1 score and accuracy, for different levels of complexity and training dataset sizes. The evaluation results indicate that the decision-tree ensemble learning algorithms provide more accurate results and outperform the DL algorithms. The performance gap reduces with the dataset complexity.
{"title":"A comparative study of deep learning and decision tree based ensemble learning algorithms for network traffic identification","authors":"Nedeljko Nikolić, S. Tomovic, I. Radusinović","doi":"10.5937/telfor2202061n","DOIUrl":"https://doi.org/10.5937/telfor2202061n","url":null,"abstract":"In this paper, we apply Deep Learning (DL) and decision-tree-based ensemble learning algorithms to classify network traffic by application. Various Deep Learning (DL) models for network traffic identification have been presented, implemented and compared, including 1D convolutional, stacked autoencoder, multi-layer perceptron, and combination of the aforementioned. Then the results of DL models have been compared to those obtained with two popular ensemble learning models based on decision trees-Random Forest and XGBoost. To train and test the classification models, a dataset containing both encrypted and unencrypted traffic has been collected in a real network, under normal operating conditions, and pre-processed in a way that ensures non-biased results. The classification uncertainties of the models have been also quantified on publicly available ISCX VPN-nonVPN dataset. The models have been compared in terms of precision, recall, F1 score and accuracy, for different levels of complexity and training dataset sizes. The evaluation results indicate that the decision-tree ensemble learning algorithms provide more accurate results and outperform the DL algorithms. The performance gap reduces with the dataset complexity.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71140877","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}
The purpose of this article is to review new trends in monitoring the condition of oil on all factory area processes. New solutions are being introduced into this industry with new advantages in the development of artificial intelligence, as well as machine learning and sensor technologies, which are applicable for data-based maintenance. They are called predictive maintenance. This paradigm is going to replace the old one. It changes the traditional routine preventive maintenance scheme and provides a deep understanding of the equipment performance [1]. Monitoring and checkout of conditions are necessary to maintain in a real-time environment because on-line control of equipment status can put down an operating cost, by eliminating the need for equipment outage for everyday diagnostics. The analysis based on oil samples is an effective tribotechnical systems approach for early diagnosis of failures, as it contains valuable information about the process of degradation of oil and the state of tribotechnical pairs [2]. But there are some problems with this method. The first is the way of oil sampling. There are lots of mistakes that may be made during the oil sampling process, and they can affect the results. The second is a delivery to laboratory which complicates the diagnostic process. That's why we cannot say this approach is an on-line method of diagnostics. For the better prognosis of pending machinery failure one needs to know a real-time correlation between size, shapes, and concentration of wear debris parts [3].
{"title":"Role of sensors in the paradigm of industry 4.0 and IIoT","authors":"A. Porokhnya, Ilia Yakimenko","doi":"10.5937/telfor2202091p","DOIUrl":"https://doi.org/10.5937/telfor2202091p","url":null,"abstract":"The purpose of this article is to review new trends in monitoring the condition of oil on all factory area processes. New solutions are being introduced into this industry with new advantages in the development of artificial intelligence, as well as machine learning and sensor technologies, which are applicable for data-based maintenance. They are called predictive maintenance. This paradigm is going to replace the old one. It changes the traditional routine preventive maintenance scheme and provides a deep understanding of the equipment performance [1]. Monitoring and checkout of conditions are necessary to maintain in a real-time environment because on-line control of equipment status can put down an operating cost, by eliminating the need for equipment outage for everyday diagnostics. The analysis based on oil samples is an effective tribotechnical systems approach for early diagnosis of failures, as it contains valuable information about the process of degradation of oil and the state of tribotechnical pairs [2]. But there are some problems with this method. The first is the way of oil sampling. There are lots of mistakes that may be made during the oil sampling process, and they can affect the results. The second is a delivery to laboratory which complicates the diagnostic process. That's why we cannot say this approach is an on-line method of diagnostics. For the better prognosis of pending machinery failure one needs to know a real-time correlation between size, shapes, and concentration of wear debris parts [3].","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71141126","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}
In this paper we continue our study of path discovery process for Service Function Chaining (SFC) in Software Defined Network (SDN). By default, service function (SF) paths are established proactively - before data transmission takes place. We have argued that this constraint can be eliminated with the use of our proposed Reactive SF path discovery approach. Such SFs as network address translation (NAT) or stateful firewall (FW) are SF path's symmetry dependent requiring a visit of both ingress and egress flows. Thus, we evaluated SF path discovery processes in Mininet emulation network. Outcome of this study is a comparison of proactive and reactive SF path discovery processes for both asymmetrical and symmetrical SF paths. It shows that even in symmetrical environment reactive SF path discovery has a higher probability of successful SF path detection.
{"title":"Evaluation of reactive service function path discovery in symmetrical environment","authors":"M. Mihaeljans, A. Skrastins","doi":"10.5937/telfor2201002m","DOIUrl":"https://doi.org/10.5937/telfor2201002m","url":null,"abstract":"In this paper we continue our study of path discovery process for Service Function Chaining (SFC) in Software Defined Network (SDN). By default, service function (SF) paths are established proactively - before data transmission takes place. We have argued that this constraint can be eliminated with the use of our proposed Reactive SF path discovery approach. Such SFs as network address translation (NAT) or stateful firewall (FW) are SF path's symmetry dependent requiring a visit of both ingress and egress flows. Thus, we evaluated SF path discovery processes in Mininet emulation network. Outcome of this study is a comparison of proactive and reactive SF path discovery processes for both asymmetrical and symmetrical SF paths. It shows that even in symmetrical environment reactive SF path discovery has a higher probability of successful SF path detection.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71140453","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}
P. I. Galanis, K. Savvas, V. A. Chernov, A. M. Butakova
From Pharmacology to Cryptography and from Geology to Astronomy are some of the scientific fields in which Quantum Computing potentially will take off and fly high. Big Quantum Computing vendors invest a large amount of money in improving the hardware and they claim that soon enough a quantum program will be hundreds of thousands of times faster than a typical one we know nowadays. But still the reliability of such systems is the main obstacle. In this work, the reliability of real quantum devices is tested and techniques of noise and error correction are presented while measurement error mitigation is explored. In addition, a well-known string matching algorithm (Bernstein-Vazirani) was applied to the real quantum computing device in order to measure its accuracy and reliability. Simulated environments were also used in order to evaluate the results. The results obtained, even if these were not 100% accurate, are very promising which proves that even these days a quantum computer working side by side with a typical one is reliable and especially when error mitigation techniques are applied.
{"title":"Reliability testing, noise and error correction of real quantum computing devices","authors":"P. I. Galanis, K. Savvas, V. A. Chernov, A. M. Butakova","doi":"10.5937/telfor2101041g","DOIUrl":"https://doi.org/10.5937/telfor2101041g","url":null,"abstract":"From Pharmacology to Cryptography and from Geology to Astronomy are some of the scientific fields in which Quantum Computing potentially will take off and fly high. Big Quantum Computing vendors invest a large amount of money in improving the hardware and they claim that soon enough a quantum program will be hundreds of thousands of times faster than a typical one we know nowadays. But still the reliability of such systems is the main obstacle. In this work, the reliability of real quantum devices is tested and techniques of noise and error correction are presented while measurement error mitigation is explored. In addition, a well-known string matching algorithm (Bernstein-Vazirani) was applied to the real quantum computing device in order to measure its accuracy and reliability. Simulated environments were also used in order to evaluate the results. The results obtained, even if these were not 100% accurate, are very promising which proves that even these days a quantum computer working side by side with a typical one is reliable and especially when error mitigation techniques are applied.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71140114","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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