Pub Date : 2017-07-01DOI: 10.1109/SMAP.2017.8022676
Marinos Theodorakopoulos, Nikos Papageorgopoulos, A. Mourti, Angeliki Antoniou, Manolis Wallace, George Lepouras, C. Vassilakis, N. Platis
In this paper we examine the suitability of the Google Cardboard as a means for the delivery of personalized cultural experiences. Specifically, we develop the content and create the application required in order to provide highly personalized visits to the Archaeological Museum in Tripolis, Greece. We also examine the usability issues related to the use of Google Cardboards. Early results are promising, and based on them we also outline the next steps ahead.
{"title":"Personalized augmented reality experiences in museums using Google Cardboards","authors":"Marinos Theodorakopoulos, Nikos Papageorgopoulos, A. Mourti, Angeliki Antoniou, Manolis Wallace, George Lepouras, C. Vassilakis, N. Platis","doi":"10.1109/SMAP.2017.8022676","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022676","url":null,"abstract":"In this paper we examine the suitability of the Google Cardboard as a means for the delivery of personalized cultural experiences. Specifically, we develop the content and create the application required in order to provide highly personalized visits to the Archaeological Museum in Tripolis, Greece. We also examine the usability issues related to the use of Google Cardboards. Early results are promising, and based on them we also outline the next steps ahead.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131807924","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 : 2017-07-01DOI: 10.1109/SMAP.2017.8022674
Louis Deladiennée, Y. Naudet
Offering personalised recommendations to visitors of a museum is a complex problem inherent to physical spaces. When at the same time specific applicative or museum objectives have to be taken into account, this becomes even more complicated. We introduce here a graph-based semantic recommender approach relying on ontological formalisation of knowledge about manipulated entities to solve the multi-dimensional recommendation problem encountered in museums.
{"title":"A graph-based semantic recommender system for a reflective and personalised museum visit: Extended abstract","authors":"Louis Deladiennée, Y. Naudet","doi":"10.1109/SMAP.2017.8022674","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022674","url":null,"abstract":"Offering personalised recommendations to visitors of a museum is a complex problem inherent to physical spaces. When at the same time specific applicative or museum objectives have to be taken into account, this becomes even more complicated. We introduce here a graph-based semantic recommender approach relying on ontological formalisation of knowledge about manipulated entities to solve the multi-dimensional recommendation problem encountered in museums.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132184520","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 : 2017-07-01DOI: 10.1109/SMAP.2017.8022658
Patrik Hlavac, Marián Simko
In this paper, we propose a method for detecting genuinely read parts of documents based on gaze data from eye tracker. This work deals with the possibilities of identifying user interaction with (web-based) documents. Our algorithm takes into account user's eye fixation information and maps their coordinates onto word-level elements. These are then processed with respect to their relative word distance. Unlike studies that calculate distance in points that eyes moved around the screen, we consider the distance of words in the word sequence. We evaluate our approach by conducting a user study that shows promising results.
{"title":"Detecting genuinely read parts of web documents","authors":"Patrik Hlavac, Marián Simko","doi":"10.1109/SMAP.2017.8022658","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022658","url":null,"abstract":"In this paper, we propose a method for detecting genuinely read parts of documents based on gaze data from eye tracker. This work deals with the possibilities of identifying user interaction with (web-based) documents. Our algorithm takes into account user's eye fixation information and maps their coordinates onto word-level elements. These are then processed with respect to their relative word distance. Unlike studies that calculate distance in points that eyes moved around the screen, we consider the distance of words in the word sequence. We evaluate our approach by conducting a user study that shows promising results.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128501414","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 : 2017-07-01DOI: 10.1109/SMAP.2017.8022666
N. Tsapatsoulis, Marilena Agathokleous
In many Voting Advice Applications (VAAs) a supplementary question concerning the voting intention of a VAA user is included. The data that are collected through this question can serve a variety of purposes, election forecast being one of them. However, it appears that the majority of VAA users who answer this question select safe choices such as “I prefer not to say” and “I am undecided”. In this study we investigate at what degree we can predict, with the aid of machine learning techniques, the voting intention of the above-mentioned users using as input their choices in the VAA policy statements. The results show an accuracy higher than 60%, supposed that sufficient training examples for each party that participates in the elections exist so as to model each party users. Also, it appears that there is significant difference on the distribution per party for the users who select “I prefer not to say” and those who select “I am undecided”. As a consequence of these findings one would suggest that for effective election forecast it is required to (a) distribute the VAA users who select the previously mentioned choices in the voting intention question in a more sophisticated and intelligent way than that followed in traditional poll methods, and (b) the VAA users who select each one of those choices should be handled separately.
{"title":"Forecasting elections from VAA data: What the undecided would vote?","authors":"N. Tsapatsoulis, Marilena Agathokleous","doi":"10.1109/SMAP.2017.8022666","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022666","url":null,"abstract":"In many Voting Advice Applications (VAAs) a supplementary question concerning the voting intention of a VAA user is included. The data that are collected through this question can serve a variety of purposes, election forecast being one of them. However, it appears that the majority of VAA users who answer this question select safe choices such as “I prefer not to say” and “I am undecided”. In this study we investigate at what degree we can predict, with the aid of machine learning techniques, the voting intention of the above-mentioned users using as input their choices in the VAA policy statements. The results show an accuracy higher than 60%, supposed that sufficient training examples for each party that participates in the elections exist so as to model each party users. Also, it appears that there is significant difference on the distribution per party for the users who select “I prefer not to say” and those who select “I am undecided”. As a consequence of these findings one would suggest that for effective election forecast it is required to (a) distribute the VAA users who select the previously mentioned choices in the voting intention question in a more sophisticated and intelligent way than that followed in traditional poll methods, and (b) the VAA users who select each one of those choices should be handled separately.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127576101","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 : 2017-07-01DOI: 10.1109/SMAP.2017.8022677
Eirini-Eleni Tsiropoulou, Athina Thanou, S. Paruchuri, S. Papavassiliou
This paper introduces a self-organizing museum visitor communities' formation exploiting their personal characteristics and social interactions, aiming at enhancing their visiting experience based on a participatory action research (PAR) process. Initially, visitors' (a) interest and social ties, (b) expertise and willingness for participation in communities and (c) physical ties, are captured towards formulating their communities, and selecting the facilitator of each community. The latter will lead the PAR process, the outcome of which will be adopted by the members of each community. A museum touring framework is proposed towards maximizing visitors' perceived Quality of Experience (QoE), while three different community formation alternatives are studied and evaluated.
{"title":"Self-organizing museum visitor communities: A participatory action research based approach","authors":"Eirini-Eleni Tsiropoulou, Athina Thanou, S. Paruchuri, S. Papavassiliou","doi":"10.1109/SMAP.2017.8022677","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022677","url":null,"abstract":"This paper introduces a self-organizing museum visitor communities' formation exploiting their personal characteristics and social interactions, aiming at enhancing their visiting experience based on a participatory action research (PAR) process. Initially, visitors' (a) interest and social ties, (b) expertise and willingness for participation in communities and (c) physical ties, are captured towards formulating their communities, and selecting the facilitator of each community. The latter will lead the PAR process, the outcome of which will be adopted by the members of each community. A museum touring framework is proposed towards maximizing visitors' perceived Quality of Experience (QoE), while three different community formation alternatives are studied and evaluated.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120881321","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 : 2017-07-01DOI: 10.1109/SMAP.2017.8022670
Dimitrios Antonaras, C. Pavlidis, N. Vretos, P. Daras
Previous studies of robots used in learning environments suggest that the interaction between learner and robot is able to enhance the learning procedure towards a better engagement of the learner. Moreover, intelligent robots can also adapt their behavior during a learning process according to certain criteria resulting in increasing cognitive learning gains. Motivated by these results, we propose a novel Human Robot Interaction framework where the robot adjusts its behavior to the affect state of the learner. Our framework uses the theory of flow to label different affect states (i.e., engagement, boredom and frustration) and adapt the robot's actions. Based on the automatic recognition of these states, through visual cues, our method adapt the learning actions taking place at this moment and performed by the robot. This results in keeping the learner at most times engaged in the learning process. In order to recognizing the affect state of the user a two step approach is followed. Initially we recognize the facial expressions of the learner and therefore we map these to an affect state. Our algorithm perform well even in situations where the environment is noisy due to the presence of more than one person and/or situations where the face is partially occluded.
{"title":"Affect state recognition for adaptive human robot interaction in learning environments","authors":"Dimitrios Antonaras, C. Pavlidis, N. Vretos, P. Daras","doi":"10.1109/SMAP.2017.8022670","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022670","url":null,"abstract":"Previous studies of robots used in learning environments suggest that the interaction between learner and robot is able to enhance the learning procedure towards a better engagement of the learner. Moreover, intelligent robots can also adapt their behavior during a learning process according to certain criteria resulting in increasing cognitive learning gains. Motivated by these results, we propose a novel Human Robot Interaction framework where the robot adjusts its behavior to the affect state of the learner. Our framework uses the theory of flow to label different affect states (i.e., engagement, boredom and frustration) and adapt the robot's actions. Based on the automatic recognition of these states, through visual cues, our method adapt the learning actions taking place at this moment and performed by the robot. This results in keeping the learner at most times engaged in the learning process. In order to recognizing the affect state of the user a two step approach is followed. Initially we recognize the facial expressions of the learner and therefore we map these to an affect state. Our algorithm perform well even in situations where the environment is noisy due to the presence of more than one person and/or situations where the face is partially occluded.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"39 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132468640","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 : 2017-07-01DOI: 10.1109/SMAP.2017.8022668
D. Vogiatzis, A. Keros
We propose an efficient community detection algorithm for networks that comprise more than one entities, such as users, tags and items, with ternary or higher relations between them. Such networks are also known as multi-partite and can be used for representing social tagging systems but also the activity in streaming media. Detecting communities in multi-paritite networks entails different challenges than in simple networks. The proposed algorithm is able to detect crisp or overlapping communities, and is applied on four data sets from social tagging systems and Twitter, and is compared with other multi-partite community detection algorithms.
{"title":"A density based algorithm for community detection in hyper-networks","authors":"D. Vogiatzis, A. Keros","doi":"10.1109/SMAP.2017.8022668","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022668","url":null,"abstract":"We propose an efficient community detection algorithm for networks that comprise more than one entities, such as users, tags and items, with ternary or higher relations between them. Such networks are also known as multi-partite and can be used for representing social tagging systems but also the activity in streaming media. Detecting communities in multi-paritite networks entails different challenges than in simple networks. The proposed algorithm is able to detect crisp or overlapping communities, and is applied on four data sets from social tagging systems and Twitter, and is compared with other multi-partite community detection algorithms.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134099153","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 : 2017-07-01DOI: 10.1109/SMAP.2017.8022675
M. Konstantakis, Konstantinos Michalakis, John Aliprantis, Eirini Kalatha, G. Caridakis
User Experience (UX) is considered a subjective and universal concept which contributes to the success of any Information and Communications Technology (ICT) framework. However, in both Information Systems and Cultural Technology research, little attention has been paid to the evaluation of UX with technologies in cultural heritage environments. Since Cultural User Experience (CUX) is an important factor, a formal classification of how to design for and evaluate CUX is necessary. This paper attempts to analyze and evaluate the aspects of CUX methodologies that are currently available and to specify future designing improvements for UX evaluation methods.
{"title":"Formalising and evaluating Cultural User Experience","authors":"M. Konstantakis, Konstantinos Michalakis, John Aliprantis, Eirini Kalatha, G. Caridakis","doi":"10.1109/SMAP.2017.8022675","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022675","url":null,"abstract":"User Experience (UX) is considered a subjective and universal concept which contributes to the success of any Information and Communications Technology (ICT) framework. However, in both Information Systems and Cultural Technology research, little attention has been paid to the evaluation of UX with technologies in cultural heritage environments. Since Cultural User Experience (CUX) is an important factor, a formal classification of how to design for and evaluate CUX is necessary. This paper attempts to analyze and evaluate the aspects of CUX methodologies that are currently available and to specify future designing improvements for UX evaluation methods.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134388807","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 : 2017-07-01DOI: 10.1109/SMAP.2017.8022672
E. Spyrou, Theodoros Giannakopoulos, Dimitris Sgouropoulos, Michalis Papakostas
Recognition of humans' emotions may be crucial in certain applications involving e.g., human-computer interaction, monitoring of elderly, understanding the affective state of learners during a course etc. To this goal and depending on the application and the environment, one may use physiological parameters (e.g., heart rate, brain activity etc.) which are typically obtrusive, or analyze other modalities that may be extracted by simply observing a human, such as visual (e.g., her/his facial expressions, gestures, skeletal motion etc.) or audio (e.g., speech). In many applications the only available modality is the latter one, i.e., the human's voice. In this work we aim to analyze a speaker's emotions by relying only on paralinguistic information, extracted by her/his voice, thus discarding the linguistic aspect of speech (i.e., the spoken words). To this goal, we propose a novel emotion classification approach that has been inspired by computer vision tasks. We use a spectrogram, which is a visual representation of the spectrum of an audio segment. We then extract features and code them using a visual vocabulary and represent a spectrogram as a “bag-of-visual words.” This representation is used for classifying an audio segment to an emotion class. We evaluate our approach on 3 datasets that contain speech from different languages and compare it to baseline methods.
{"title":"Extracting emotions from speech using a bag-of-visual-words approach","authors":"E. Spyrou, Theodoros Giannakopoulos, Dimitris Sgouropoulos, Michalis Papakostas","doi":"10.1109/SMAP.2017.8022672","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022672","url":null,"abstract":"Recognition of humans' emotions may be crucial in certain applications involving e.g., human-computer interaction, monitoring of elderly, understanding the affective state of learners during a course etc. To this goal and depending on the application and the environment, one may use physiological parameters (e.g., heart rate, brain activity etc.) which are typically obtrusive, or analyze other modalities that may be extracted by simply observing a human, such as visual (e.g., her/his facial expressions, gestures, skeletal motion etc.) or audio (e.g., speech). In many applications the only available modality is the latter one, i.e., the human's voice. In this work we aim to analyze a speaker's emotions by relying only on paralinguistic information, extracted by her/his voice, thus discarding the linguistic aspect of speech (i.e., the spoken words). To this goal, we propose a novel emotion classification approach that has been inspired by computer vision tasks. We use a spectrogram, which is a visual representation of the spectrum of an audio segment. We then extract features and code them using a visual vocabulary and represent a spectrogram as a “bag-of-visual words.” This representation is used for classifying an audio segment to an emotion class. We evaluate our approach on 3 datasets that contain speech from different languages and compare it to baseline methods.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129032658","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 : 2017-07-01DOI: 10.1109/SMAP.2017.8022669
Xuan Wu, Dong Zhou, Yu Xu, S. Lawless
Social tagging systems have been widely used as a way to annotate and categorize Web resources. However, users often use unrestricted vocabulary to tag and describe resources. On the contrast, annotators of Web documents may use very different words to describe the same concept. In the past few years, numerous personalized query expansion methods have been proposed to tackle the vocabulary mismatch problem. Many of them are based on the probabilistic-based techniques or graph-based techniques, but they ignored the multi-relational characteristics existed in the social data. In this paper, we explore multiple semantic relationships from social tagging systems, including relationships between tags, between words and between tags and words. Three affinity graphs are built based on the features derived from tags and words. In addition, we incorporate pseudo-relevance feedback information obtained from top-ranked documents to regularize the smoothness of multiple associations over the three affinity graphs. The key of this paper is considering above three affinity graphs into a novel query expansion model and aim to produce better personalized search results. Experiments conducted on a real-world dataset validate the effectiveness of the proposed approach.
{"title":"Personalized query expansion utilizing multi-relational social data","authors":"Xuan Wu, Dong Zhou, Yu Xu, S. Lawless","doi":"10.1109/SMAP.2017.8022669","DOIUrl":"https://doi.org/10.1109/SMAP.2017.8022669","url":null,"abstract":"Social tagging systems have been widely used as a way to annotate and categorize Web resources. However, users often use unrestricted vocabulary to tag and describe resources. On the contrast, annotators of Web documents may use very different words to describe the same concept. In the past few years, numerous personalized query expansion methods have been proposed to tackle the vocabulary mismatch problem. Many of them are based on the probabilistic-based techniques or graph-based techniques, but they ignored the multi-relational characteristics existed in the social data. In this paper, we explore multiple semantic relationships from social tagging systems, including relationships between tags, between words and between tags and words. Three affinity graphs are built based on the features derived from tags and words. In addition, we incorporate pseudo-relevance feedback information obtained from top-ranked documents to regularize the smoothness of multiple associations over the three affinity graphs. The key of this paper is considering above three affinity graphs into a novel query expansion model and aim to produce better personalized search results. Experiments conducted on a real-world dataset validate the effectiveness of the proposed approach.","PeriodicalId":441461,"journal":{"name":"2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116039974","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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