This work addresses the problem of fusing spatio-temporal uncertainties obtained from heterogeneous location sources: on-board GPS devices and roadside sensors. We develop a model for combining the uncertain location-values from the different sources, which further narrows the possible locations of a given object. Our experiments demonstrate that the proposed model may eliminate significant amount of the false positives, compared to the traditional space-time prism (bead) uncertainty models.
{"title":"The tale of (fusing) two uncertainties","authors":"Bingxin Zhang, Goce Trajcevski","doi":"10.1145/2666310.2666495","DOIUrl":"https://doi.org/10.1145/2666310.2666495","url":null,"abstract":"This work addresses the problem of fusing spatio-temporal uncertainties obtained from heterogeneous location sources: on-board GPS devices and roadside sensors. We develop a model for combining the uncertain location-values from the different sources, which further narrows the possible locations of a given object. Our experiments demonstrate that the proposed model may eliminate significant amount of the false positives, compared to the traditional space-time prism (bead) uncertainty models.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124614124","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}
We define a topology-based distance metric between road networks embedded in the plane. This distance measure is based on local persistent homology, and employs a local distance signature that enables identification and visualization of local differences between the road networks. This paper is motivated by the need to recognize changes in road networks over time and to assess the quality of different map construction algorithms. One particular challenge is evaluating the results when no ground truth is known. However, we demonstrate that we can overcome this hurdle by using a statistical technique known as the bootstrap.
{"title":"Local persistent homology based distance between maps","authors":"M. Ahmed, Brittany Terese Fasy, C. Wenk","doi":"10.1145/2666310.2666390","DOIUrl":"https://doi.org/10.1145/2666310.2666390","url":null,"abstract":"We define a topology-based distance metric between road networks embedded in the plane. This distance measure is based on local persistent homology, and employs a local distance signature that enables identification and visualization of local differences between the road networks. This paper is motivated by the need to recognize changes in road networks over time and to assess the quality of different map construction algorithms. One particular challenge is evaluating the results when no ground truth is known. However, we demonstrate that we can overcome this hurdle by using a statistical technique known as the bootstrap.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121921012","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}
M. Omori, Masaharu Hirota, H. Ishikawa, Shohei Yokoyama
Many photos shared on photo-sharing sites are annotated with tags and geo-tags. Some studies have demonstrated extraction of the geographical characterization which a tag represents as regions using those metadata. However, in some cases (e.g. coastline), a line is more suitable than a region as a geographical characterization of a tag. Therefore, we proposed a novel method to extract lines as a region as a geographical characterization. Results show that the distance of a coastline and many lines of our method is less than 500 m. Although, in this paper, only the coastline has been evaluated, this method is applicable to other tags as well.
{"title":"Can geo-tags on flickr draw coastlines?","authors":"M. Omori, Masaharu Hirota, H. Ishikawa, Shohei Yokoyama","doi":"10.1145/2666310.2666436","DOIUrl":"https://doi.org/10.1145/2666310.2666436","url":null,"abstract":"Many photos shared on photo-sharing sites are annotated with tags and geo-tags. Some studies have demonstrated extraction of the geographical characterization which a tag represents as regions using those metadata. However, in some cases (e.g. coastline), a line is more suitable than a region as a geographical characterization of a tag. Therefore, we proposed a novel method to extract lines as a region as a geographical characterization. Results show that the distance of a coastline and many lines of our method is less than 500 m. Although, in this paper, only the coastline has been evaluated, this method is applicable to other tags as well.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133684836","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}
Amin Gheibi, A. Maheshwari, J. Sack, Christian Scheffer
We propose a new measure to capture similarity between polygonal curves, called the minimum backward Fréchet distance. It is a natural optimization on the weak Fréchet distance, a variant of the well-known Fréchet distance. More specifically, for a given threshold ε, we are searching for a pair of walks for two entities on the two input curves, T1 and T2, such that the union of the portions of backward movements is minimized and the distance between the two entities, at any time during the walk, is less than or equal to ε. Our algorithm detects if no such pair of walks exists. This natural optimization problem appears in many applications in Geographical Information Systems, mobile networks and robotics. We provide an exact algorithm with time complexity of O(n2 log n) and space complexity of O(n2), where n is the maximum number of segments in the input polygonal curves.
{"title":"Minimum backward fréchet distance","authors":"Amin Gheibi, A. Maheshwari, J. Sack, Christian Scheffer","doi":"10.1145/2666310.2666418","DOIUrl":"https://doi.org/10.1145/2666310.2666418","url":null,"abstract":"We propose a new measure to capture similarity between polygonal curves, called the minimum backward Fréchet distance. It is a natural optimization on the weak Fréchet distance, a variant of the well-known Fréchet distance. More specifically, for a given threshold ε, we are searching for a pair of walks for two entities on the two input curves, T1 and T2, such that the union of the portions of backward movements is minimized and the distance between the two entities, at any time during the walk, is less than or equal to ε. Our algorithm detects if no such pair of walks exists. This natural optimization problem appears in many applications in Geographical Information Systems, mobile networks and robotics. We provide an exact algorithm with time complexity of O(n2 log n) and space complexity of O(n2), where n is the maximum number of segments in the input polygonal curves.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129061080","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}
D. Laptev, Alexey Tikhonov, P. Serdyukov, Gleb Gusev
The task of discovering places of interest is a key step for many location-based recommendation tasks. In this paper we propose a fully unsupervised and parameter-free approach to deal with this problem based on the collection of geotagged photos. While previous papers are mostly devoted to discovering points (POI), we focus on areas of interest (AOI). Recommendation of better matches the traditional tourist goals and allows to robustly incorporate the interests of many users resulting in less subjective recommendations. The typical question that can be answered with the algorithm is formulated as "Where can one spend T minutes/hours walking around to observe as many attractive places as possible?" The proposed method starts with estimating multiple density hypotheses and then partitions these densities with the watershed segmentation algorithm into regions. The implicit parameters are tuned automatically to fit tourist goals and constraints resulting in a parameter-free algorithm. In spite of the parameter optimization overhead, the method is computationally efficient as it employs fast Fourier transforms for convolutions. We test our approach on 7 different cities and quantitatively show that the proposed method consistently outperforms the state-of-the-art DBSCAN algorithm and its modern modification P-DBSCAN, providing up to several times better recommendations in terms of time required for city exploration.
{"title":"Parameter-free discovery and recommendation of areas-of-interest","authors":"D. Laptev, Alexey Tikhonov, P. Serdyukov, Gleb Gusev","doi":"10.1145/2666310.2666416","DOIUrl":"https://doi.org/10.1145/2666310.2666416","url":null,"abstract":"The task of discovering places of interest is a key step for many location-based recommendation tasks. In this paper we propose a fully unsupervised and parameter-free approach to deal with this problem based on the collection of geotagged photos. While previous papers are mostly devoted to discovering points (POI), we focus on areas of interest (AOI). Recommendation of better matches the traditional tourist goals and allows to robustly incorporate the interests of many users resulting in less subjective recommendations. The typical question that can be answered with the algorithm is formulated as \"Where can one spend T minutes/hours walking around to observe as many attractive places as possible?\" The proposed method starts with estimating multiple density hypotheses and then partitions these densities with the watershed segmentation algorithm into regions. The implicit parameters are tuned automatically to fit tourist goals and constraints resulting in a parameter-free algorithm. In spite of the parameter optimization overhead, the method is computationally efficient as it employs fast Fourier transforms for convolutions. We test our approach on 7 different cities and quantitatively show that the proposed method consistently outperforms the state-of-the-art DBSCAN algorithm and its modern modification P-DBSCAN, providing up to several times better recommendations in terms of time required for city exploration.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129775578","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}
Real-life spatial databases are inherently incomplete. This is in particular the case when data from different sources are combined. An extreme example are volunteered geographical information systems like OpenStreetMap. When querying such databases the question arises how reliable are the retrieved answers. For instance, for positive queries, which ask for existing patterns of objects, further answers could show up if the data is completed. For queries with negation, it is furthermore possible that after data completion objects cease to satisfy a query. On the OpenStreetMap wiki, contributors have started to record for some areas which object types have been mapped completely. Given a query, we show how such metainformation can be used to classify objects in the database as certain answers, which are certainly answers in reality, impossible answers, which in reality are definitely not answers, and possible answers, for which it is not known whether they are answers in reality. In addition, we compute the completeness area of a query, that is the maximal area for which it is certain that no further answer objects exist in reality. All this additional information can be computed with standard operations on spatial data. Experiments suggest that the computation of such completeness information is feasible.
{"title":"Adding completeness information to query answers over spatial databases","authors":"Simon Razniewski, W. Nutt","doi":"10.1145/2666310.2666395","DOIUrl":"https://doi.org/10.1145/2666310.2666395","url":null,"abstract":"Real-life spatial databases are inherently incomplete. This is in particular the case when data from different sources are combined. An extreme example are volunteered geographical information systems like OpenStreetMap. When querying such databases the question arises how reliable are the retrieved answers. For instance, for positive queries, which ask for existing patterns of objects, further answers could show up if the data is completed. For queries with negation, it is furthermore possible that after data completion objects cease to satisfy a query. On the OpenStreetMap wiki, contributors have started to record for some areas which object types have been mapped completely. Given a query, we show how such metainformation can be used to classify objects in the database as certain answers, which are certainly answers in reality, impossible answers, which in reality are definitely not answers, and possible answers, for which it is not known whether they are answers in reality. In addition, we compute the completeness area of a query, that is the maximal area for which it is certain that no further answer objects exist in reality. All this additional information can be computed with standard operations on spatial data. Experiments suggest that the computation of such completeness information is feasible.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132642089","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}
We describe an online algorithm to simplify large volumes of location and sensor data on the source mobile device, by eliminating redundant data points and saving important ones. Our approach is to use topological persistence to identify large scale sharp features of a data stream. We show that for one-dimensional data streams such as trajectories, simplification based on topologically persistent features can be maintained online, such that each new data-point is processed in O(1) time. Our method extends to multi-resolution simplifications, where it identifies larger scale features that represent more important elements of data, and naturally eliminates noise and small deviations. The multi-resolution simplification is also maintained online in real time, at cost of O(1) per input point. Therefore it is lightweight and suitable for use in embedded sensors and mobile phones. The method can be applied to more general data streams such as sensor data to produce similar simplifications. Our experiments on real data show that this approach when applied to the curvature function of trajectory or sensor data produces compact simplifications with low approximation errors comparable to existing offline methods.
{"title":"Persistence based online signal and trajectory simplification for mobile devices","authors":"P. Katsikouli, Rik Sarkar, Jie Gao","doi":"10.1145/2666310.2666388","DOIUrl":"https://doi.org/10.1145/2666310.2666388","url":null,"abstract":"We describe an online algorithm to simplify large volumes of location and sensor data on the source mobile device, by eliminating redundant data points and saving important ones. Our approach is to use topological persistence to identify large scale sharp features of a data stream. We show that for one-dimensional data streams such as trajectories, simplification based on topologically persistent features can be maintained online, such that each new data-point is processed in O(1) time. Our method extends to multi-resolution simplifications, where it identifies larger scale features that represent more important elements of data, and naturally eliminates noise and small deviations. The multi-resolution simplification is also maintained online in real time, at cost of O(1) per input point. Therefore it is lightweight and suitable for use in embedded sensors and mobile phones. The method can be applied to more general data streams such as sensor data to produce similar simplifications. Our experiments on real data show that this approach when applied to the curvature function of trajectory or sensor data produces compact simplifications with low approximation errors comparable to existing offline methods.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122875757","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}
We consider the application of route planning in large public-transportation networks (buses, trains, subways, etc). Many connections in such networks are operated at periodic time intervals. When a set of connections has sufficient periodicity, it becomes more efficient to store the time range and frequency (e.g., every 15 minutes from 8:00am-6:00pm) instead of storing each of the time events separately. Identifying an optimal frequency-compression is NP-hard, so we present a time- and space-efficient heuristic. We show how we can use this compression to not only save space but also query time. We particularly consider profile queries, which ask for all optimal routes with departure times in a given interval (e.g., a whole day). In particular, we design a new version of Dijkstra's algorithm that works with frequency-based labels and is suitable for profile queries. We evaluate the savings of our approach on two metropolitan and three country-wide public-transportation networks. On our largest network, we simultaneously achieve a better space consumption than all previous methods as well as profile query times that are about 5 times faster than the best previous method. We also improve Transfer Patterns, a state-of-the-art technique for fully realistic route planning in large public-transportation networks. In particular, we accelerate the expensive preprocessing by a factor of 60 compared to the original publication.
{"title":"Frequency-based search for public transit","authors":"H. Bast, Sabine Storandt","doi":"10.1145/2666310.2666405","DOIUrl":"https://doi.org/10.1145/2666310.2666405","url":null,"abstract":"We consider the application of route planning in large public-transportation networks (buses, trains, subways, etc). Many connections in such networks are operated at periodic time intervals. When a set of connections has sufficient periodicity, it becomes more efficient to store the time range and frequency (e.g., every 15 minutes from 8:00am-6:00pm) instead of storing each of the time events separately. Identifying an optimal frequency-compression is NP-hard, so we present a time- and space-efficient heuristic. We show how we can use this compression to not only save space but also query time. We particularly consider profile queries, which ask for all optimal routes with departure times in a given interval (e.g., a whole day). In particular, we design a new version of Dijkstra's algorithm that works with frequency-based labels and is suitable for profile queries. We evaluate the savings of our approach on two metropolitan and three country-wide public-transportation networks. On our largest network, we simultaneously achieve a better space consumption than all previous methods as well as profile query times that are about 5 times faster than the best previous method. We also improve Transfer Patterns, a state-of-the-art technique for fully realistic route planning in large public-transportation networks. In particular, we accelerate the expensive preprocessing by a factor of 60 compared to the original publication.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117091253","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}
Map labels provide valuable geographic information by annotating geographic phenomenona with text descriptions. However, many interesting and useful maps are only available as images and hence this information is not readily accessible in a Geographic Information System (GIS). Previous work on text recognition in maps considers maps as a special type of image to be processed using Optical Character Recognition (OCR) techniques and does not pay attention to the typical workflows in a GIS. As a result, to convert map labels into machine-readable text, a user has to switch between OCR and GIS software, transform the detected text locations from the image coordinates (in OCR) to the map coordinates (in GIS), and apply data import/export procedures. This tedious process limits the opportunity to access text information in maps. This paper presents ArcStrabo, an integration of our previous text recognition work and a GIS, which uses a GIS user interface, workflows, and data types to enable efficient training of text recognition algorithms for converting map labels to a table of geographic names. We show that ArcStrabo facilitates map digitization processes, eliminates the need for GIS users to learn additional OCR tools, and does not require manual data export/import procedures between GIS and OCR software.
{"title":"From map images to geographic names","authors":"Yao-Yi Chiang, Sima Moghaddam, Sanjauli Gupta, Renuka Fernandes, Craig A. Knoblock","doi":"10.1145/2666310.2666374","DOIUrl":"https://doi.org/10.1145/2666310.2666374","url":null,"abstract":"Map labels provide valuable geographic information by annotating geographic phenomenona with text descriptions. However, many interesting and useful maps are only available as images and hence this information is not readily accessible in a Geographic Information System (GIS). Previous work on text recognition in maps considers maps as a special type of image to be processed using Optical Character Recognition (OCR) techniques and does not pay attention to the typical workflows in a GIS. As a result, to convert map labels into machine-readable text, a user has to switch between OCR and GIS software, transform the detected text locations from the image coordinates (in OCR) to the map coordinates (in GIS), and apply data import/export procedures. This tedious process limits the opportunity to access text information in maps. This paper presents ArcStrabo, an integration of our previous text recognition work and a GIS, which uses a GIS user interface, workflows, and data types to enable efficient training of text recognition algorithms for converting map labels to a table of geographic names. We show that ArcStrabo facilitates map digitization processes, eliminates the need for GIS users to learn additional OCR tools, and does not require manual data export/import procedures between GIS and OCR software.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116707229","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}
Advances in geographic information extraction have exposed previously untapped resources, such as many travel itineraries found in HTML tables and spreadsheets on the Web. In the general sense, itineraries differ from the related concepts of routes and trajectories in that the precise paths between stopping points are of far less importance than the locations of the stopping points and their order. This characteristic allows for some flexibility when visualizing itineraries. A method for automatically generating itinerary visualizations is presented, which utilizes principles from graph-drawing and map labeling, along with additional criteria designed specifically for the itinerary visualization task. We describe a system based on this method that can perform automated layout for arbitrary itineraries at varying scales.
{"title":"Automated tabular itinerary visualization","authors":"M. Adelfio, H. Samet","doi":"10.1145/2666310.2666377","DOIUrl":"https://doi.org/10.1145/2666310.2666377","url":null,"abstract":"Advances in geographic information extraction have exposed previously untapped resources, such as many travel itineraries found in HTML tables and spreadsheets on the Web. In the general sense, itineraries differ from the related concepts of routes and trajectories in that the precise paths between stopping points are of far less importance than the locations of the stopping points and their order. This characteristic allows for some flexibility when visualizing itineraries. A method for automatically generating itinerary visualizations is presented, which utilizes principles from graph-drawing and map labeling, along with additional criteria designed specifically for the itinerary visualization task. We describe a system based on this method that can perform automated layout for arbitrary itineraries at varying scales.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115075623","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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