Pub Date : 2019-06-01DOI: 10.1109/iacs.2019.8809168
{"title":"ICICS 2019 Message from the General Chair","authors":"","doi":"10.1109/iacs.2019.8809168","DOIUrl":"https://doi.org/10.1109/iacs.2019.8809168","url":null,"abstract":"","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"279 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":"123432936","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.2019.8809083
Suleyman Al-Showarah
The way people interact with handheld devices such as smartphone and tablet tends heavily dependent on age and experience. It can argued that the automatic identification of an age group or a level of user’s experience based on the way they are using the devices could contribute greatly to providing adaptive usage environment for each user. This study aims to investigate the effectiveness of employing the dynamic features generated by users of smartphones and tablets to automatically recognise their age group. To achieve that we created a database of over 2520 trials from 42 participants of elderly (60+) and younger users (20-39) using finger based handwriting of 10 different English words. The user recognition consists of three stages: collecting touch hand writing data, extracting features, and classification. Handwriting on touchscreen data was collected on two sizes of smartphones devices based finger. The features used were force pressure (FP), movement time (MT), and signature precision (SP). In the training dataset, the feature’s average for each trial of 6 across 10 words was calculated. A KNN classification is used to classify user age. The study considered number of users in the training dataset for 100%, 50%, and one user (i.e. 1%). The results revealed there were high classification accuracy on small smartphone compared to mini-tablet. The classification accuracy using the combined features for all users on the training dataset was (82%) on small smartphone and (77%) on mini-tablet. We found that the classification of younger users (95%) were more accurate than the elderly users (55%). The study provides an evidence of the possibility of classifying user age group based on hand writing words on touchscreen based finger.
{"title":"Dynamic Recognition for User Age-Group Classification Using Hand-Writing Based Finger on Smartphones","authors":"Suleyman Al-Showarah","doi":"10.1109/IACS.2019.8809083","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809083","url":null,"abstract":"The way people interact with handheld devices such as smartphone and tablet tends heavily dependent on age and experience. It can argued that the automatic identification of an age group or a level of user’s experience based on the way they are using the devices could contribute greatly to providing adaptive usage environment for each user. This study aims to investigate the effectiveness of employing the dynamic features generated by users of smartphones and tablets to automatically recognise their age group. To achieve that we created a database of over 2520 trials from 42 participants of elderly (60+) and younger users (20-39) using finger based handwriting of 10 different English words. The user recognition consists of three stages: collecting touch hand writing data, extracting features, and classification. Handwriting on touchscreen data was collected on two sizes of smartphones devices based finger. The features used were force pressure (FP), movement time (MT), and signature precision (SP). In the training dataset, the feature’s average for each trial of 6 across 10 words was calculated. A KNN classification is used to classify user age. The study considered number of users in the training dataset for 100%, 50%, and one user (i.e. 1%). The results revealed there were high classification accuracy on small smartphone compared to mini-tablet. The classification accuracy using the combined features for all users on the training dataset was (82%) on small smartphone and (77%) on mini-tablet. We found that the classification of younger users (95%) were more accurate than the elderly users (55%). The study provides an evidence of the possibility of classifying user age group based on hand writing words on touchscreen based finger.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"23 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":"125168720","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.2019.8809105
O. Banimelhem, Hiba Al-Dahoud, E. Taqieddin, M. Mowafi
Database as a service is one of the important services provided by Cloud Computing. Recently, a Two Layered Protection Scheme for securing the database has been proposed. The scheme employs two symmetric key encryption algorithms, the Order Preserving Encryption and Format Preserving Encryption. Each of the two encryption algorithms uses a different encryption key that is derived from Key Splitting module. Key Splitting module generates two keys from a Main Key by using a randomized algorithm. Randomized algorithm does not guarantee that the generated keys are always different because the resulting keys depend on the generated random numbers. Hence, in order to increase the security of the Two Layered Protection Scheme, a Genetic based Key Splitting algorithm is proposed. The purpose of the proposed algorithm is to generate the best random two keys that have maximum difference so that the two keys cannot be derived from each other. Simulation results have shown that the proposed algorithm generates random keys with maximum difference.
{"title":"Genetic-based Key Splitting Algorithm for the Two Layered Protection Scheme","authors":"O. Banimelhem, Hiba Al-Dahoud, E. Taqieddin, M. Mowafi","doi":"10.1109/IACS.2019.8809105","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809105","url":null,"abstract":"Database as a service is one of the important services provided by Cloud Computing. Recently, a Two Layered Protection Scheme for securing the database has been proposed. The scheme employs two symmetric key encryption algorithms, the Order Preserving Encryption and Format Preserving Encryption. Each of the two encryption algorithms uses a different encryption key that is derived from Key Splitting module. Key Splitting module generates two keys from a Main Key by using a randomized algorithm. Randomized algorithm does not guarantee that the generated keys are always different because the resulting keys depend on the generated random numbers. Hence, in order to increase the security of the Two Layered Protection Scheme, a Genetic based Key Splitting algorithm is proposed. The purpose of the proposed algorithm is to generate the best random two keys that have maximum difference so that the two keys cannot be derived from each other. Simulation results have shown that the proposed algorithm generates random keys with maximum difference.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"10 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":"125208002","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.2019.8809139
{"title":"ICICS 2019 Index","authors":"","doi":"10.1109/iacs.2019.8809139","DOIUrl":"https://doi.org/10.1109/iacs.2019.8809139","url":null,"abstract":"","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"43 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":"125384145","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.2019.8809140
Awos Kanan, F. Gebali, Atef Ibrahim, K. F. Li
Processor array architecture is a popular approach to improve computation of similarity distance matrices; however, most of the proposed architectures are designed in an ad hoc manner, some have not even considered dimensionality and size of the datasets. We believe a systematic approach is necessary to explore the design space. In this work, we present a technique for designing scalable processor array architecture for the similarity distance matrix computation. Implementation results of the proposed architecture show improved compromise between area and speed. Moreover, it scales better for large and high-dimensional datasets since the architecture is fully parameterized and only has to deal with one data dimension in each time step.
{"title":"Scalable and Parameterizable Processor Array Architecture for Similarity Distance Computation","authors":"Awos Kanan, F. Gebali, Atef Ibrahim, K. F. Li","doi":"10.1109/IACS.2019.8809140","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809140","url":null,"abstract":"Processor array architecture is a popular approach to improve computation of similarity distance matrices; however, most of the proposed architectures are designed in an ad hoc manner, some have not even considered dimensionality and size of the datasets. We believe a systematic approach is necessary to explore the design space. In this work, we present a technique for designing scalable processor array architecture for the similarity distance matrix computation. Implementation results of the proposed architecture show improved compromise between area and speed. Moreover, it scales better for large and high-dimensional datasets since the architecture is fully parameterized and only has to deal with one data dimension in each time step.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"9 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":"125537293","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.2019.8809106
Yasmeen Shaher Alsalman, Nancy Khamees Abu Halemah, Eman Alnagi, W. Salameh
Student Academic Performance is a great concern for academic institutions in all levels of academic years. Techniques like classification, clustering and association are provided by Data Mining. In this paper, two classification techniques, Decision Tree (J48) and Artificial Neural Network (ANN), are used to build a classification model, that can predict the academic performance of university students in Jordan, expected GPA in precise. A dataset has been gathered using online questionnaire, and certain attributes were selected to test their relevance to the academic performance of a Jordanian university students. The paper describes the methodology conducted to apply the J48 and ANN, using a special tool (WEKA), and the results are discussed in details, showing a better performance for ANN in some cases, and a better performance for Decision Tree in others.
{"title":"Using Decision Tree and Artificial Neural Network to Predict Students Academic Performance","authors":"Yasmeen Shaher Alsalman, Nancy Khamees Abu Halemah, Eman Alnagi, W. Salameh","doi":"10.1109/IACS.2019.8809106","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809106","url":null,"abstract":"Student Academic Performance is a great concern for academic institutions in all levels of academic years. Techniques like classification, clustering and association are provided by Data Mining. In this paper, two classification techniques, Decision Tree (J48) and Artificial Neural Network (ANN), are used to build a classification model, that can predict the academic performance of university students in Jordan, expected GPA in precise. A dataset has been gathered using online questionnaire, and certain attributes were selected to test their relevance to the academic performance of a Jordanian university students. The paper describes the methodology conducted to apply the J48 and ANN, using a special tool (WEKA), and the results are discussed in details, showing a better performance for ANN in some cases, and a better performance for Decision Tree in others.","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":"126539756","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.2019.8809129
Audi I. Al-Btoush, M. Abbadi, Ahmad Hassanat, A. Tarawneh, Asad Hasanat, V. Prasath
Due to their large number of applications, eye-tracking systems have gain attention recently. In this work, we propose 4 new features to support the most used feature by these systems, which is the location (x, y). These features are based on the white areas in the four corners of the sclera; the ratio of the whites area (after segmentation) to the corners area is used as a feature coming from each corner. In order to evaluate the new features, we designed a simple eye-tracking system using a simple webcam, where the users faces and eyes are detected, which allows for extracting the traditional and the new features. The system was evaluated using 10 subjects, who looked at 5 objects on the screen. The experimental results using some machine learning algorithms show that the new features are user dependent, and therefore, they cannot be used (in their current format) for a multiuser eye-tracking system. However, the new features might be used to support the traditional features for a better single-user eye-tracking system, where the accuracy results were in the range of 0.90 to 0.98.
{"title":"New Features for Eye-Tracking Systems: Preliminary Results","authors":"Audi I. Al-Btoush, M. Abbadi, Ahmad Hassanat, A. Tarawneh, Asad Hasanat, V. Prasath","doi":"10.1109/IACS.2019.8809129","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809129","url":null,"abstract":"Due to their large number of applications, eye-tracking systems have gain attention recently. In this work, we propose 4 new features to support the most used feature by these systems, which is the location (x, y). These features are based on the white areas in the four corners of the sclera; the ratio of the whites area (after segmentation) to the corners area is used as a feature coming from each corner. In order to evaluate the new features, we designed a simple eye-tracking system using a simple webcam, where the users faces and eyes are detected, which allows for extracting the traditional and the new features. The system was evaluated using 10 subjects, who looked at 5 objects on the screen. The experimental results using some machine learning algorithms show that the new features are user dependent, and therefore, they cannot be used (in their current format) for a multiuser eye-tracking system. However, the new features might be used to support the traditional features for a better single-user eye-tracking system, where the accuracy results were in the range of 0.90 to 0.98.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"16 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":"129348769","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.2019.8809107
Lana Issa, Firas Alghanim, Nadim Obeid
Computer and cognitive scientists are making serious attempts via developing algorithms which aim to simulate some aspects of human’s cognitive behavior.In this context, it is not surprising that research efforts are directed towards studying human’s creativity in an attempt to understand how humans produce creative content, and in what sense does it differ from cognitive content.The idea we are proposing in this research is that we may to a certain level, though humble, emulate some aspects of human creativity. We propose a method which involves designing a program that generates content that could be considered creative. It is important to note that we are not making any claim about matching human creativity level which we believe is limitless and cannot be measured. We shall apply our proposed program on a field that is considered as one of the creative fields in human life and that is based on the idea of combinatorial creativity. The field is culinary arts. The program has been tested to generate creative culinary content using the culinary knowledge of certain Arabic countries and modeling their culinary practice into several steps inspired by the pattern of their process of combining several ingredients.The results were satisfying where only 2 out of 255 were considered as unacceptable results. The program showed its ability in generating creative content in the field of culinary arts. We aim to expand our experiments and to generalize this program to suit several artistic domains.
{"title":"Computational Creativity: The Design of a Creative Computer Program","authors":"Lana Issa, Firas Alghanim, Nadim Obeid","doi":"10.1109/IACS.2019.8809107","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809107","url":null,"abstract":"Computer and cognitive scientists are making serious attempts via developing algorithms which aim to simulate some aspects of human’s cognitive behavior.In this context, it is not surprising that research efforts are directed towards studying human’s creativity in an attempt to understand how humans produce creative content, and in what sense does it differ from cognitive content.The idea we are proposing in this research is that we may to a certain level, though humble, emulate some aspects of human creativity. We propose a method which involves designing a program that generates content that could be considered creative. It is important to note that we are not making any claim about matching human creativity level which we believe is limitless and cannot be measured. We shall apply our proposed program on a field that is considered as one of the creative fields in human life and that is based on the idea of combinatorial creativity. The field is culinary arts. The program has been tested to generate creative culinary content using the culinary knowledge of certain Arabic countries and modeling their culinary practice into several steps inspired by the pattern of their process of combining several ingredients.The results were satisfying where only 2 out of 255 were considered as unacceptable results. The program showed its ability in generating creative content in the field of culinary arts. We aim to expand our experiments and to generalize this program to suit several artistic domains.","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":"133720013","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.2019.8809155
Saja Khaled Tawalbehe, Omar Alzoubi, Mohammad Al-Smadi
Emotion Detection (ED) from text has been an active research field recently. It has attracted the attention of researchers as it can measure the emotional contexts while humans interact with computers. Humans could express their emotion in various ways; using typed text, facial expressions, speech, gestures, and physiological measures. ED is considerably different from sentiment analysis SA, where SA goal is to detect polarity from text such as positive, negative or neutral. On the other hand, ED aims to recognize emotions from input text. Emotions can be modeled as discrete categories, e.g. Ekmans six basic emotions (angry, fear, joy, disgust, surprise and sadness). On the other hand there is the dimensional model that express emotions as valence, arousal and dominance values. Social media provides a rich source of emotional text, e.g. Twitter and Facebook. In this paper, we provide a review of recent work on ED from Arabic text. We discuss approaches (lexicons, machine learning, deep neural networks and ensemble approaches), tools for text processing, and We also discuss description of the most popular datasets in this domain.
{"title":"Recent Advances of Affect Detection from Arabic Text","authors":"Saja Khaled Tawalbehe, Omar Alzoubi, Mohammad Al-Smadi","doi":"10.1109/IACS.2019.8809155","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809155","url":null,"abstract":"Emotion Detection (ED) from text has been an active research field recently. It has attracted the attention of researchers as it can measure the emotional contexts while humans interact with computers. Humans could express their emotion in various ways; using typed text, facial expressions, speech, gestures, and physiological measures. ED is considerably different from sentiment analysis SA, where SA goal is to detect polarity from text such as positive, negative or neutral. On the other hand, ED aims to recognize emotions from input text. Emotions can be modeled as discrete categories, e.g. Ekmans six basic emotions (angry, fear, joy, disgust, surprise and sadness). On the other hand there is the dimensional model that express emotions as valence, arousal and dominance values. Social media provides a rich source of emotional text, e.g. Twitter and Facebook. In this paper, we provide a review of recent work on ED from Arabic text. We discuss approaches (lexicons, machine learning, deep neural networks and ensemble approaches), tools for text processing, and We also discuss description of the most popular datasets in this domain.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"11 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":"130753978","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.2019.8809080
Mohammad Alshamaileh, Ahmad Hassanat, A. Tarawneh, M. Sohel Rahman, C. Celik, Moohanad Jawthari
Word-based writer identification and authentication have been under investigation for years. In this paper, we present a new Arabic online/offline handwriting dataset for writer authentication and identification. The created dataset includes two parts: AHWDB1 and AHWDB2 which are made freely available for the research community. Each part of the dataset consists of 2000 (10 trials X 200 writers) samples captured using HUAWEI MediaPad M3. Dynamic information such as pressure, timestamp and the coordinates(X, Y) have been collected and involved for both parts of the dataset. In addition, age, gender and education level have been added to the dataset for future investigation. Several experiments are conducted on the dataset. The preliminary identification results are obtained using K-Nearest Neighbor (KNN) with Dynamic time warping (DTW) and support vector machines (SVM). We recorded 81.35% identification rate as the best result using SVM classifier on AHWDB2.
{"title":"New Online/Offline text-dependent Arabic Handwriting dataset for Writer Authentication and Identification","authors":"Mohammad Alshamaileh, Ahmad Hassanat, A. Tarawneh, M. Sohel Rahman, C. Celik, Moohanad Jawthari","doi":"10.1109/IACS.2019.8809080","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809080","url":null,"abstract":"Word-based writer identification and authentication have been under investigation for years. In this paper, we present a new Arabic online/offline handwriting dataset for writer authentication and identification. The created dataset includes two parts: AHWDB1 and AHWDB2 which are made freely available for the research community. Each part of the dataset consists of 2000 (10 trials X 200 writers) samples captured using HUAWEI MediaPad M3. Dynamic information such as pressure, timestamp and the coordinates(X, Y) have been collected and involved for both parts of the dataset. In addition, age, gender and education level have been added to the dataset for future investigation. Several experiments are conducted on the dataset. The preliminary identification results are obtained using K-Nearest Neighbor (KNN) with Dynamic time warping (DTW) and support vector machines (SVM). We recorded 81.35% identification rate as the best result using SVM classifier on AHWDB2.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"131 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":"132829884","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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