Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020964
W. Torrez, D. Bamber, I. Goodman, H. Nguyen
There is an obvious need to be able to integrate both linguistic-based and stochastic-based input information in data fusion. In particular, this need is critical in addressing problems of track association, including cyber-state intrusions. This paper treats this issue through a new insight into how three apparently distinct mathematical tools can be combined: "boolean relational event algebra" (BREA), "one point random set coverage representations of fuzzy sets" (OPRSC), and "complexity-reducing algorithm for near optimal fusion" (CRANOF).
{"title":"A new method for representing linguistic quantifications by random sets with applications to tracking and data fusion","authors":"W. Torrez, D. Bamber, I. Goodman, H. Nguyen","doi":"10.1109/ICIF.2002.1020964","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020964","url":null,"abstract":"There is an obvious need to be able to integrate both linguistic-based and stochastic-based input information in data fusion. In particular, this need is critical in addressing problems of track association, including cyber-state intrusions. This paper treats this issue through a new insight into how three apparently distinct mathematical tools can be combined: \"boolean relational event algebra\" (BREA), \"one point random set coverage representations of fuzzy sets\" (OPRSC), and \"complexity-reducing algorithm for near optimal fusion\" (CRANOF).","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122300403","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020983
Wen-Ran Zhang
It is observed that Boolean logic is a unipolar logic defined in the unipolar space {0,1}. It is argued that a unipolar system cannot be directly used to represent and reason with the coexistence of bipolar truth. To circumvent the representational and reasoning limitations of unipolar systems, a 4-valued bipolar combinational logic BCL is introduced based on the ancient Chinese Yin-Yang philosophy. The new logic is defined in a strict bipolar space S = {- 1,0}/spl times/{0,1}, which is proved a generalization of Boolean logic and a fusion of two interactive unipolar subsystems. Bipolar tautologies including modus ponens are introduced for bipolar inference. The semantics of the new logic is established, justified, and compared with unipolar systems. Bipolar relations, bipolar transitivity, and polarized reflexivity are introduced. An O(n/sup 3/) algorithm is presented for bipolar transitive closure computation. In addition, the lair's case in the ancient paradox is redressed based on bipolar logic and bipolar relations.
{"title":"Bipolar logic and bipolar knowledge fusion","authors":"Wen-Ran Zhang","doi":"10.1109/ICIF.2002.1020983","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020983","url":null,"abstract":"It is observed that Boolean logic is a unipolar logic defined in the unipolar space {0,1}. It is argued that a unipolar system cannot be directly used to represent and reason with the coexistence of bipolar truth. To circumvent the representational and reasoning limitations of unipolar systems, a 4-valued bipolar combinational logic BCL is introduced based on the ancient Chinese Yin-Yang philosophy. The new logic is defined in a strict bipolar space S = {- 1,0}/spl times/{0,1}, which is proved a generalization of Boolean logic and a fusion of two interactive unipolar subsystems. Bipolar tautologies including modus ponens are introduced for bipolar inference. The semantics of the new logic is established, justified, and compared with unipolar systems. Bipolar relations, bipolar transitivity, and polarized reflexivity are introduced. An O(n/sup 3/) algorithm is presented for bipolar transitive closure computation. In addition, the lair's case in the ancient paradox is redressed based on bipolar logic and bipolar relations.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116530328","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1021189
A. Gad, M. Farooq
Various multisensor data fusion architectures have been utilized to support the Maritime Surveillance (MS) in maritime tactical and strategic operations. The military tactical situation is mechanized through data fusion thus improving the quality of target tracking system. One of the major problems is that the surveillance area is generally large, hence making it difficult to arrive at a feasible data fusion architecture. The latter arises due to timing, accuracy, and different types of sensors and sensor platforms. In this paper, various data fusion architectures for MS are discussed. The proposed system interacts with the data fusion processes at different information levels. This architecture is employed to support the MS for a typical maritime tactical scenario. The proposed architecture has an acceptable performance.
{"title":"Data fusion architecture for Maritime Surveillance","authors":"A. Gad, M. Farooq","doi":"10.1109/ICIF.2002.1021189","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021189","url":null,"abstract":"Various multisensor data fusion architectures have been utilized to support the Maritime Surveillance (MS) in maritime tactical and strategic operations. The military tactical situation is mechanized through data fusion thus improving the quality of target tracking system. One of the major problems is that the surveillance area is generally large, hence making it difficult to arrive at a feasible data fusion architecture. The latter arises due to timing, accuracy, and different types of sensors and sensor platforms. In this paper, various data fusion architectures for MS are discussed. The proposed system interacts with the data fusion processes at different information levels. This architecture is employed to support the MS for a typical maritime tactical scenario. The proposed architecture has an acceptable performance.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124359736","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1021202
Tanzeem Chaodhury, James M. Rehg, V. Pavlovic, A. Pentland
Bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with efficient algorithms for inference and learning. Temporal fusion of multiple sensors can be efficiently formulated using dynamic Bayesian networks (DBNs) which allow the power of statistical inference and learning to be combined with contextual knowledge of the problem. Unfortunately, simple learning methods can cause such appealing models to fail when the data exhibits complex behavior We first demonstrate how boosted parameter learning could be used to improve the performance of Bayesian network classifiers for complex multimodal inference problems. As an example we apply the framework to the problem of audiovisual speaker detection in an interactive environment using "off-the-shelf" visual and audio sensors (face, skin, texture, mouth motion, and silence detectors). We then introduce a boosted structure learning algorithm. Given labeled data, our algorithm modifies both the network structure and parameters so as to improve classification accuracy. We compare its performance to both standard structure learning and boosted parameter learning. We present results for speaker detection and for datasets from the UCI repository.
{"title":"Boosted learning in dynamic Bayesian networks for multimodal detection","authors":"Tanzeem Chaodhury, James M. Rehg, V. Pavlovic, A. Pentland","doi":"10.1109/ICIF.2002.1021202","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021202","url":null,"abstract":"Bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with efficient algorithms for inference and learning. Temporal fusion of multiple sensors can be efficiently formulated using dynamic Bayesian networks (DBNs) which allow the power of statistical inference and learning to be combined with contextual knowledge of the problem. Unfortunately, simple learning methods can cause such appealing models to fail when the data exhibits complex behavior We first demonstrate how boosted parameter learning could be used to improve the performance of Bayesian network classifiers for complex multimodal inference problems. As an example we apply the framework to the problem of audiovisual speaker detection in an interactive environment using \"off-the-shelf\" visual and audio sensors (face, skin, texture, mouth motion, and silence detectors). We then introduce a boosted structure learning algorithm. Given labeled data, our algorithm modifies both the network structure and parameters so as to improve classification accuracy. We compare its performance to both standard structure learning and boosted parameter learning. We present results for speaker detection and for datasets from the UCI repository.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127571588","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1021228
S. Davey, D. Gray, S. Colegrove
An important problem in multi-target tracking is track initiation and termination. The tracking algorithm aims to discriminate false detections caused by various sources of interference from valid detections caused by targets of interest. This is a problem of model order estimation. One approach to solving this problem with the Probabilistic Data Association Filter has been referred to as target visibility. This paper shows how the target visibility model can be incorporated into the Probabilistic Multi-Hypothesis Tracker to provide integrated initiation and termination.
{"title":"A Markov model for initiating tracks with the probabilistic multi-hypothesis tracker","authors":"S. Davey, D. Gray, S. Colegrove","doi":"10.1109/ICIF.2002.1021228","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021228","url":null,"abstract":"An important problem in multi-target tracking is track initiation and termination. The tracking algorithm aims to discriminate false detections caused by various sources of interference from valid detections caused by targets of interest. This is a problem of model order estimation. One approach to solving this problem with the Probabilistic Data Association Filter has been referred to as target visibility. This paper shows how the target visibility model can be incorporated into the Probabilistic Multi-Hypothesis Tracker to provide integrated initiation and termination.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128816450","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020900
E. Besada-Portas, J. A. López-Orozco, Jesus M. de la Cruz
A multisensor fusion system that is used for estimating the location of a robot and the state of the objects around it is presented. The whole fusion system has been implemented as a dynamic Bayesian network (DBN) with the purpose of having a homogenous and formalized way of capturing the dependencies that exist between robot location, the state of the environment, and all sensorial data. At this stage of research it consists of two independent DBNs, one for estimating robot location and another for building an occupancy probabilistic map of the environment, which are the basis of a unified fusion system. The dependencies of the variables and information in the two DBNs are captured by a unique DBN constructed by adding arcs (and nodes if necessary) between the two DBNs. The DBN implemented so far can be used in robots with different sets of sensors.
{"title":"Unified fusion system based on Bayesian networks for autonomous mobile robots","authors":"E. Besada-Portas, J. A. López-Orozco, Jesus M. de la Cruz","doi":"10.1109/ICIF.2002.1020900","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020900","url":null,"abstract":"A multisensor fusion system that is used for estimating the location of a robot and the state of the objects around it is presented. The whole fusion system has been implemented as a dynamic Bayesian network (DBN) with the purpose of having a homogenous and formalized way of capturing the dependencies that exist between robot location, the state of the environment, and all sensorial data. At this stage of research it consists of two independent DBNs, one for estimating robot location and another for building an occupancy probabilistic map of the environment, which are the basis of a unified fusion system. The dependencies of the variables and information in the two DBNs are captured by a unique DBN constructed by adding arcs (and nodes if necessary) between the two DBNs. The DBN implemented so far can be used in robots with different sets of sensors.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122361235","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020915
R. Kannan, S. Sarangi, S. Ray, S. S. Iyengar
We define the problem of maximal sensor integrity placement, that of locating sensors in n-dimensional grids with minimal vulnerability to enemy attack or sensor faults. We show a polynomial time algorithm for computing sensor integrity exists for sensors with unbounded ranges deployed over a 1D grid of points. We then present an integer linear programming (ILP) formulation for computing sensor integrity for unbounded range sensors over higher dimension grids.
{"title":"Minimal sensor integrity in sensor grids","authors":"R. Kannan, S. Sarangi, S. Ray, S. S. Iyengar","doi":"10.1109/ICIF.2002.1020915","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020915","url":null,"abstract":"We define the problem of maximal sensor integrity placement, that of locating sensors in n-dimensional grids with minimal vulnerability to enemy attack or sensor faults. We show a polynomial time algorithm for computing sensor integrity exists for sensors with unbounded ranges deployed over a 1D grid of points. We then present an integer linear programming (ILP) formulation for computing sensor integrity for unbounded range sensors over higher dimension grids.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133787399","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020968
J. Masters
Structured Knowledge Source Integration, or SKSI, is an ongoing research and development project at Cycorp intended to enable the Cyc knowledge base to integrate (access, query, assimilate, and merge) a variety of external structured knowledge sources, such as databases, spreadsheets, XML or DAML tagged text, GIS datasets, and queryable Web pages. With SKSI, the Cyc knowledge base will be able to draw upon information obtained from multiple knowledge sources when answering complex queries, to assimilate (transform and store) the contents of the knowledge sources directly into the Cyc knowledge base, and to mediate between several semantically similar knowledge sources. These capabilities will extend the flexibility and power of the Cyc product to serve as the universal ontology and knowledge repository in any application requiring knowledge based reasoning. This article discusses some of the main technical issues of knowledge source integration, reviews some of the literature on the subject, describes some elements of the SKSI approach, illustrates two example Cyc queries that use two structured knowledge sources already mapped into Cyc, and proposes a Schema Modeling Toolkit of applications we are designing to leverage the core SKSI development.
{"title":"Structured Knowledge Source Integration and its applications to information fusion","authors":"J. Masters","doi":"10.1109/ICIF.2002.1020968","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020968","url":null,"abstract":"Structured Knowledge Source Integration, or SKSI, is an ongoing research and development project at Cycorp intended to enable the Cyc knowledge base to integrate (access, query, assimilate, and merge) a variety of external structured knowledge sources, such as databases, spreadsheets, XML or DAML tagged text, GIS datasets, and queryable Web pages. With SKSI, the Cyc knowledge base will be able to draw upon information obtained from multiple knowledge sources when answering complex queries, to assimilate (transform and store) the contents of the knowledge sources directly into the Cyc knowledge base, and to mediate between several semantically similar knowledge sources. These capabilities will extend the flexibility and power of the Cyc product to serve as the universal ontology and knowledge repository in any application requiring knowledge based reasoning. This article discusses some of the main technical issues of knowledge source integration, reviews some of the literature on the subject, describes some elements of the SKSI approach, illustrates two example Cyc queries that use two structured knowledge sources already mapped into Cyc, and proposes a Schema Modeling Toolkit of applications we are designing to leverage the core SKSI development.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130364308","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020948
Paolo Remagnino, Graeme A. Jones
The fusion of tracking and classification information in multi-camera surveillance environments will result in greater robustness, accuracy and temporal extent of interpretation of activity within the monitored scene. Crucial to such fusion is the recovery of the camera calibration which allows such information to be expressed in a common coordinate system. Rather than relying on the traditional time-consuming, labour-intensive and expert-dependent calibration procedures to recover the camera calibration, extensible plug-and-play surveillance components should employ simple learning calibration procedures by merely watching objects entering, passing through and leaving the monitored scene. In this work we present such a two stage calibration procedure. In the first stage, a linear model of the projected height of objects in the scene is used in conjunction with world knowledge about the average person height to recover the image-plane to local-ground-plane transformation of each camera. In the second stage, a Hough transform technique is used to recover the transformations between these local ground planes.
{"title":"Automated registration of surveillance data for multi-camera fusion","authors":"Paolo Remagnino, Graeme A. Jones","doi":"10.1109/ICIF.2002.1020948","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020948","url":null,"abstract":"The fusion of tracking and classification information in multi-camera surveillance environments will result in greater robustness, accuracy and temporal extent of interpretation of activity within the monitored scene. Crucial to such fusion is the recovery of the camera calibration which allows such information to be expressed in a common coordinate system. Rather than relying on the traditional time-consuming, labour-intensive and expert-dependent calibration procedures to recover the camera calibration, extensible plug-and-play surveillance components should employ simple learning calibration procedures by merely watching objects entering, passing through and leaving the monitored scene. In this work we present such a two stage calibration procedure. In the first stage, a linear model of the projected height of objects in the scene is used in conjunction with world knowledge about the average person height to recover the image-plane to local-ground-plane transformation of each camera. In the second stage, a Hough transform technique is used to recover the transformations between these local ground planes.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132385520","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1021199
Kuo-Chu Chang, Z. Tian
A Bayesian network is a compact representation for probabilistic models and inference. They have been used successfully for multisensor fusion and situation assessment. It is well known that, in general, the inference algorithms to compute the exact posterior probability of the target state are either computationally infeasible for dense networks or impossible for mixed discrete continuous networks. In those cases, one approach is to compute the approximate results using simulation methods. This paper proposes efficient inference methods for those cases. The goal is not to compute the exact or approximate posterior probability of the target state, but to identify the top (most likely) ones in an efficient manner. The approach is to use intelligent simulation techniques where previous samples will be used to guide the future sampling strategy. By focusing the sampling on the "important" space, we are able to sort out the top candidates quickly. Simulation results are included to demonstrate the performances of the algorithms.
{"title":"Efficient inference for mixed Bayesian networks","authors":"Kuo-Chu Chang, Z. Tian","doi":"10.1109/ICIF.2002.1021199","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021199","url":null,"abstract":"A Bayesian network is a compact representation for probabilistic models and inference. They have been used successfully for multisensor fusion and situation assessment. It is well known that, in general, the inference algorithms to compute the exact posterior probability of the target state are either computationally infeasible for dense networks or impossible for mixed discrete continuous networks. In those cases, one approach is to compute the approximate results using simulation methods. This paper proposes efficient inference methods for those cases. The goal is not to compute the exact or approximate posterior probability of the target state, but to identify the top (most likely) ones in an efficient manner. The approach is to use intelligent simulation techniques where previous samples will be used to guide the future sampling strategy. By focusing the sampling on the \"important\" space, we are able to sort out the top candidates quickly. Simulation results are included to demonstrate the performances of the algorithms.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132388919","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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