Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034648
Pratama Mahadika, Aries Subiantoro
Increasing fuel price has forced automotive manufacturers to innovate in increasing fuel efficiency. Hybrid vehicles, especially Parallel Hybrid Electric Vehicle configuration have been proven to be able to improve fuel efficiency. The main key in term of fuel efficiency of hybrid vehicles is the controller of the Energy Management System (EMS) that manages the performance of the engine and motor so that the vehicle can work in the optimal working range. In this paper, the controller is designed by using shortest path algorithm to find control sequences with the most efficient cost to finish a drive cycle. Because the controller purposed in this paper is using open loop model, then this method can be used as a benchmark to be compared with another method. The result of this study is this method can be used to control EMS of parallel hybrid car efficiently.
{"title":"Design of Optimal Controller for Parallel Hybrid Electric Vehicle Based On Shortest Path Algorithm","authors":"Pratama Mahadika, Aries Subiantoro","doi":"10.1109/ISRITI48646.2019.9034648","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034648","url":null,"abstract":"Increasing fuel price has forced automotive manufacturers to innovate in increasing fuel efficiency. Hybrid vehicles, especially Parallel Hybrid Electric Vehicle configuration have been proven to be able to improve fuel efficiency. The main key in term of fuel efficiency of hybrid vehicles is the controller of the Energy Management System (EMS) that manages the performance of the engine and motor so that the vehicle can work in the optimal working range. In this paper, the controller is designed by using shortest path algorithm to find control sequences with the most efficient cost to finish a drive cycle. Because the controller purposed in this paper is using open loop model, then this method can be used as a benchmark to be compared with another method. The result of this study is this method can be used to control EMS of parallel hybrid car efficiently.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"8 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132364997","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034567
Muhammad Husain Toding Bunga, S. Suyanto
As technologies of natural language understanding and generation improve, the human interest towards human-computer interaction increases. The technologies can be applied for various applications of customer services. Most works related to this field are emphasizing on single sentence and speaker turn. Meanwhile, a conversation sometimes has its own context according to the previous one. Designing this kind of conversational system is challenging. Most conversational agents are built based on knowledge-based and rule based systems. This paper discusses a development of a complete dialogue system to understand the intent of a text and give response based on the dialogue state. The dialogue model is implemented using the combination of rule-based and data-driven approach by utilizing a long short-term memory (LSTM). Some experiments show that the developed system give a high performance. A detail observation informs that some errors come from the intent classifier that fails to classify some sentences not in the corpus. This system can be improved by increasing the performance of the intent classifier and incorporating an additional named entity recognition module.
{"title":"Developing a Complete Dialogue System Using Long Short-Term Memory","authors":"Muhammad Husain Toding Bunga, S. Suyanto","doi":"10.1109/ISRITI48646.2019.9034567","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034567","url":null,"abstract":"As technologies of natural language understanding and generation improve, the human interest towards human-computer interaction increases. The technologies can be applied for various applications of customer services. Most works related to this field are emphasizing on single sentence and speaker turn. Meanwhile, a conversation sometimes has its own context according to the previous one. Designing this kind of conversational system is challenging. Most conversational agents are built based on knowledge-based and rule based systems. This paper discusses a development of a complete dialogue system to understand the intent of a text and give response based on the dialogue state. The dialogue model is implemented using the combination of rule-based and data-driven approach by utilizing a long short-term memory (LSTM). Some experiments show that the developed system give a high performance. A detail observation informs that some errors come from the intent classifier that fails to classify some sentences not in the corpus. This system can be improved by increasing the performance of the intent classifier and incorporating an additional named entity recognition module.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126232968","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 : 2019-12-01DOI: 10.1109/isriti48646.2019.9034636
{"title":"ISRITI 2019 Sponsors","authors":"","doi":"10.1109/isriti48646.2019.9034636","DOIUrl":"https://doi.org/10.1109/isriti48646.2019.9034636","url":null,"abstract":"","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122353914","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034604
Michael Shane, Lukman Wisnudrajat, Sfenrianto Sfenrianto, Tanty Oktavianty
The purpose of this study is to identify the factors that influence people to shop online at Shopee based on e-business value creation. There are many online marketplaces available in Indonesia, with Shopee being one of the fastest growing e-commerce players in Indonesia. This research aims to find out Shopee’s customers demographic, and e-business value creation factors that influence consumers’ intention to shop online using Shopee. Based on the data collected, the majority of Shopee’s customers are in the 25-34 years old and 35-44 years age old group, and on average they spend less than 1 million Rupiah per month at Shopee. Out of all factors in the e-business value creation, the research shows that all factors (Complementarities, Lock-In, Novelty, and Efficiency) positively affect intention to use Shopee.
{"title":"E-Business Value Creation Factors that Affect Consumers’ Intention to Shop Online at Shopee.co.id","authors":"Michael Shane, Lukman Wisnudrajat, Sfenrianto Sfenrianto, Tanty Oktavianty","doi":"10.1109/ISRITI48646.2019.9034604","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034604","url":null,"abstract":"The purpose of this study is to identify the factors that influence people to shop online at Shopee based on e-business value creation. There are many online marketplaces available in Indonesia, with Shopee being one of the fastest growing e-commerce players in Indonesia. This research aims to find out Shopee’s customers demographic, and e-business value creation factors that influence consumers’ intention to shop online using Shopee. Based on the data collected, the majority of Shopee’s customers are in the 25-34 years old and 35-44 years age old group, and on average they spend less than 1 million Rupiah per month at Shopee. Out of all factors in the e-business value creation, the research shows that all factors (Complementarities, Lock-In, Novelty, and Efficiency) positively affect intention to use Shopee.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121474030","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 : 2019-12-01DOI: 10.1109/isriti48646.2019.9034623
{"title":"ISRITI 2019 Technical Program Committee","authors":"","doi":"10.1109/isriti48646.2019.9034623","DOIUrl":"https://doi.org/10.1109/isriti48646.2019.9034623","url":null,"abstract":"","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122574005","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034587
Linda Yunita, A. H. Saputro, B. Kiswanjaya
A system that could help a medical practitioner to diagnose a patient who is smoker or nonsmoker is needed. Smoker's melanosis could be used as one indicator to identify someone is a smoker or not. This study focuses on the development of a noninvasive system of smoker identification based on hyperspectral imaging. The developed system consists of a smoker's image acquisition instrument and image processing algorithm using spectral and spatial characteristics in the Visible and Near-Infrared (VNIR) range. The average pixel intensity at a spatial range is used as a feature that represents the relative reflectance at the wavelength of 400 – 1000 nm. The PCA method is used to reduce the dimensions (features) into five characteristic features. The SVM method is used to classify the feature into Smoker's Melanosis (SM) and normal pixel information. This experiment was using 45 samples consisting of 20 smokers and 25 nonsmokers. It was performed to test the performance of the developed system. The results show that the accuracy is 97.31%, misclassification rate (MR) is 2.69%, false-positive rate (FPR) is 0%, false-negative rate (FNR) is 5.83%, sensitivity is 94.17%, and specificity is 100%. In general, the system has worked to help diagnose a smoker.
{"title":"Smoker’s Melanosis Tongue Identification System using the Spatial and Spectral Characteristic Combinations Tongue in the Visible and Near-Infrared Range","authors":"Linda Yunita, A. H. Saputro, B. Kiswanjaya","doi":"10.1109/ISRITI48646.2019.9034587","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034587","url":null,"abstract":"A system that could help a medical practitioner to diagnose a patient who is smoker or nonsmoker is needed. Smoker's melanosis could be used as one indicator to identify someone is a smoker or not. This study focuses on the development of a noninvasive system of smoker identification based on hyperspectral imaging. The developed system consists of a smoker's image acquisition instrument and image processing algorithm using spectral and spatial characteristics in the Visible and Near-Infrared (VNIR) range. The average pixel intensity at a spatial range is used as a feature that represents the relative reflectance at the wavelength of 400 – 1000 nm. The PCA method is used to reduce the dimensions (features) into five characteristic features. The SVM method is used to classify the feature into Smoker's Melanosis (SM) and normal pixel information. This experiment was using 45 samples consisting of 20 smokers and 25 nonsmokers. It was performed to test the performance of the developed system. The results show that the accuracy is 97.31%, misclassification rate (MR) is 2.69%, false-positive rate (FPR) is 0%, false-negative rate (FNR) is 5.83%, sensitivity is 94.17%, and specificity is 100%. In general, the system has worked to help diagnose a smoker.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129070374","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034599
A. Rozie, Andria Arisal, D. Munandar
Analyzing sentiment analysis with deep learning requires massive labeled datasets where such data is not always available. The annotation process is also time-consuming and tedious. Further, even after we train the sentiment analysis, it creates another problem. Because this model is domain-dependent, the performance in another domain estimated to perform poorly. In this paper, we present the transfer learning approach to transfer knowledge gained from the source dataset into the target dataset with the expectation to improve the target model. Multichannel Convolutional Neural Network deploys different n-grams as the input channel in a single CNN model to grasp meaningful features from the text. This method has proven to perform well in sentiment analysis problems. We train our three datasets with different domains using this method as the baseline. The largest dataset then becomes the source model for transfer learning and other datasets as the target. Fine-tuning our source model also needed when retraining it into the target dataset. From the evaluation, we show that several transfer learning strategies outperform the domain-specific model, even when the data is imbalanced. We also highlight certain failing strategies that inflict lousy results on the target model performance.
{"title":"Transferring Multi-Channel Convolutional Neural Network Model for Cross-Domain Sentiment Analysis","authors":"A. Rozie, Andria Arisal, D. Munandar","doi":"10.1109/ISRITI48646.2019.9034599","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034599","url":null,"abstract":"Analyzing sentiment analysis with deep learning requires massive labeled datasets where such data is not always available. The annotation process is also time-consuming and tedious. Further, even after we train the sentiment analysis, it creates another problem. Because this model is domain-dependent, the performance in another domain estimated to perform poorly. In this paper, we present the transfer learning approach to transfer knowledge gained from the source dataset into the target dataset with the expectation to improve the target model. Multichannel Convolutional Neural Network deploys different n-grams as the input channel in a single CNN model to grasp meaningful features from the text. This method has proven to perform well in sentiment analysis problems. We train our three datasets with different domains using this method as the baseline. The largest dataset then becomes the source model for transfer learning and other datasets as the target. Fine-tuning our source model also needed when retraining it into the target dataset. From the evaluation, we show that several transfer learning strategies outperform the domain-specific model, even when the data is imbalanced. We also highlight certain failing strategies that inflict lousy results on the target model performance.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122792273","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034640
Hidayah Zohro’iyah, S. M. Nasution, Ratna Astuti Nugrahaeni
In this paper, we propose an implementation of Naïve Bayes algorithm in a chase game called Maze Chase. Maze Chase is a chase game where a player must avoid several chasings Non-Player Character (NPC). In our proposed implementation, the NPC will run automatically using artificial intelligence. There are four NPC in Maze Chase, each with its own characteristics. Because of the characteristic differences, the four NPC needs to communicate with each other. For communication we use a multi-agent system. Multi-agent system is a part of artificial intelligence which were used by NPC to communicate with each other using several defined parameters. We used several parameters, such as the number of coins in a zone, the amount of golden coins in a zone, and the centroid values. These parameters were used as variables for an implementation of Naïve Bayes algorithms. Our proposed implementation of Naïve Bayes was used to count the probabilities of NPC behavior, which will move closer towards the player according to several zones in the game map. From the testing results, Naïve Bayes algorithm could be used to decide the NPC movement according to its target zone on the Maze Chase game, with error rate 0.5%.
{"title":"Determining NPC Behavior in Maze Chase Game using Naïve Bayes Algorithm","authors":"Hidayah Zohro’iyah, S. M. Nasution, Ratna Astuti Nugrahaeni","doi":"10.1109/ISRITI48646.2019.9034640","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034640","url":null,"abstract":"In this paper, we propose an implementation of Naïve Bayes algorithm in a chase game called Maze Chase. Maze Chase is a chase game where a player must avoid several chasings Non-Player Character (NPC). In our proposed implementation, the NPC will run automatically using artificial intelligence. There are four NPC in Maze Chase, each with its own characteristics. Because of the characteristic differences, the four NPC needs to communicate with each other. For communication we use a multi-agent system. Multi-agent system is a part of artificial intelligence which were used by NPC to communicate with each other using several defined parameters. We used several parameters, such as the number of coins in a zone, the amount of golden coins in a zone, and the centroid values. These parameters were used as variables for an implementation of Naïve Bayes algorithms. Our proposed implementation of Naïve Bayes was used to count the probabilities of NPC behavior, which will move closer towards the player according to several zones in the game map. From the testing results, Naïve Bayes algorithm could be used to decide the NPC movement according to its target zone on the Maze Chase game, with error rate 0.5%.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123051200","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034670
R. B. Taqriban, R. Ismail, M. Ariyanto, Andika Febri Yaya Syah Putra
This paper presents the development of the manufacture of a transtibial prosthetics socket for the below-knee amputee. A technique used in this paper is using photogrammetry. The quality of the photogrammetry method is determined by measuring the error of the photogrammetry 3D model with the actual model using four different cameras. The 3D model of the transtibial socket is created by taking photos of the remaining limb of the amputee and process it in the software for the 3D generation and rectification process. 3D printing is needed to print the 3D model of the socket and then compare it to the conventional casting socket using image processing. The test will be conducted by applying the 3D printed socket to the amputee to be used for a walk.
{"title":"3D Model of Photogrammetry Technique for Transtibial Prosthetic Socket Design Development","authors":"R. B. Taqriban, R. Ismail, M. Ariyanto, Andika Febri Yaya Syah Putra","doi":"10.1109/ISRITI48646.2019.9034670","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034670","url":null,"abstract":"This paper presents the development of the manufacture of a transtibial prosthetics socket for the below-knee amputee. A technique used in this paper is using photogrammetry. The quality of the photogrammetry method is determined by measuring the error of the photogrammetry 3D model with the actual model using four different cameras. The 3D model of the transtibial socket is created by taking photos of the remaining limb of the amputee and process it in the software for the 3D generation and rectification process. 3D printing is needed to print the 3D model of the socket and then compare it to the conventional casting socket using image processing. The test will be conducted by applying the 3D printed socket to the amputee to be used for a walk.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128978914","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034593
Muhammad Ari Prayogo, Jatmiko Endro Suseno, Dinar Mutiara Kusumo Nugraheni
The importance of selecting alternative land used for palm oil cultivation requires some priority assessment of the land, therefore the need to implement a decision support system in choosing which land to plant palm oil in the future. There is no system for selecting land priority to plant palm oil purpose of this study is to develop a system for selecting palm oil cultivation land by implementing a decision support system using the Additive Ratio Assessment (ARAS) method in analyzing each alternative land criteria. The results showed that the alternative lands ranking that had the highest value AL3, which was Mendik Makmur, had the highest K value of 0.869, which placed the land Mendik Makmur in the first rank based on ARAS method calculations. The resulting accuracy testing ARAS method by comparing the ranking results with other methods against the results of expert assessments get an accuracy score of 77%, with 7 alternative ranking positions are the same as the results of the assessment of experts from a total of 9 alternatives.
{"title":"Selecting Palm Oil Cultivation Land using ARAS Method","authors":"Muhammad Ari Prayogo, Jatmiko Endro Suseno, Dinar Mutiara Kusumo Nugraheni","doi":"10.1109/ISRITI48646.2019.9034593","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034593","url":null,"abstract":"The importance of selecting alternative land used for palm oil cultivation requires some priority assessment of the land, therefore the need to implement a decision support system in choosing which land to plant palm oil in the future. There is no system for selecting land priority to plant palm oil purpose of this study is to develop a system for selecting palm oil cultivation land by implementing a decision support system using the Additive Ratio Assessment (ARAS) method in analyzing each alternative land criteria. The results showed that the alternative lands ranking that had the highest value AL3, which was Mendik Makmur, had the highest K value of 0.869, which placed the land Mendik Makmur in the first rank based on ARAS method calculations. The resulting accuracy testing ARAS method by comparing the ranking results with other methods against the results of expert assessments get an accuracy score of 77%, with 7 alternative ranking positions are the same as the results of the assessment of experts from a total of 9 alternatives.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128797231","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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