Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841262
J. Sijs, J. Fletcher
Robotic systems operating in the real world would benefit from a clear semantic model to understand their interactions with the real world. Such semantics are typically captured in an ontology. Unfortunately, the underlying model of existing ontologies requires many work-arounds before it can be used to capture general knowledge about objects and interactions in the real physical world. To remove such work-arounds, this article adopts the richer hypergraph model. It is used to develop an ontology, which is further implemented as the knowledge base of an actual robotic system performing search operations. Also, actual information extracted from the robot's sensors is used to update its knowledge base logically and sensibly.
{"title":"A robotic knowledge base to model and update real-world information from indoor environments","authors":"J. Sijs, J. Fletcher","doi":"10.23919/fusion49751.2022.9841262","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841262","url":null,"abstract":"Robotic systems operating in the real world would benefit from a clear semantic model to understand their interactions with the real world. Such semantics are typically captured in an ontology. Unfortunately, the underlying model of existing ontologies requires many work-arounds before it can be used to capture general knowledge about objects and interactions in the real physical world. To remove such work-arounds, this article adopts the richer hypergraph model. It is used to develop an ontology, which is further implemented as the knowledge base of an actual robotic system performing search operations. Also, actual information extracted from the robot's sensors is used to update its knowledge base logically and sensibly.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132408358","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841322
Daniel Frisch, U. Hanebeck
We present a quasi-Monte Carlo acceptance-rejection sampling method for arbitrary multivariate continuous probability density functions. The method employs either a uni-form or a Gaussian proposal distribution. The proposal samples are provided by optimal deterministic sampling based on the generalized Fibonacci lattice. By using low-discrepancy samples from generalized Fibonacci lattices, we achieve a more locally homogeneous sample distribution than random sampling meth-ods for arbitrary continuous densities such as the Metropolis-Hastings algorithm or slice sampling, or acceptance-rejection based on state-of-the-art quasi-random sampling methods like the Sobol or Halton sequence.
{"title":"Rejection Sampling from Arbitrary Multivariate Distributions Using Generalized Fibonacci Lattices","authors":"Daniel Frisch, U. Hanebeck","doi":"10.23919/fusion49751.2022.9841322","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841322","url":null,"abstract":"We present a quasi-Monte Carlo acceptance-rejection sampling method for arbitrary multivariate continuous probability density functions. The method employs either a uni-form or a Gaussian proposal distribution. The proposal samples are provided by optimal deterministic sampling based on the generalized Fibonacci lattice. By using low-discrepancy samples from generalized Fibonacci lattices, we achieve a more locally homogeneous sample distribution than random sampling meth-ods for arbitrary continuous densities such as the Metropolis-Hastings algorithm or slice sampling, or acceptance-rejection based on state-of-the-art quasi-random sampling methods like the Sobol or Halton sequence.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117089809","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841373
D. Crouse
Coordinate systems and coordinate system conversions for bistatic range, bistatic range-rate, and azimuthal angle measured by a surface-wave radar are presented. Expressions for the Cramer-Rao Lower Bound (CRLB) for error analysis are provided and are demonstrated in a measurement conversion scenario.
{"title":"Coordinates and Conversions for Surface-Wave Radar","authors":"D. Crouse","doi":"10.23919/fusion49751.2022.9841373","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841373","url":null,"abstract":"Coordinate systems and coordinate system conversions for bistatic range, bistatic range-rate, and azimuthal angle measured by a surface-wave radar are presented. Expressions for the Cramer-Rao Lower Bound (CRLB) for error analysis are provided and are demonstrated in a measurement conversion scenario.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123971118","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841319
Rong Yang, Y. Bar-Shalom, H. Huang
This paper considers a camera calibration problem using a discrete-time drone trajectory recorded by an accurate GPS. The challenge is that the GPS receiver and camera are not time synchronized (there is an unknown time offset between the two systems). The problem is formulated as an estimation problem to estimate the parameter vector consisting of the three camera orientation angles and the time offset. The estimation is based on the camera measurements and the discrete time GPS trajectory. The maximum likelihood (ML) estimator using the Iterated Least Squares (ILS) algorithm is developed. It can estimate the parameter vector in continuous space using discrete-time GPS information. Simulation tests are conducted on three drone trajectories. The estimation accuracy achieves the CRLB, and thus it is statistically efficient. The results are further analyzed from the point of view of real impact: the residual bias error (following the calibration) should not be significant compared to the camera measurement error (noise standard deviation). The most suitable drone trajectory is therefore recommended among the three. Its bias error is 24% of the measurement error.
{"title":"Camera Calibration with Unknown Time Offset between the Camera and Drone GPS Systems","authors":"Rong Yang, Y. Bar-Shalom, H. Huang","doi":"10.23919/fusion49751.2022.9841319","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841319","url":null,"abstract":"This paper considers a camera calibration problem using a discrete-time drone trajectory recorded by an accurate GPS. The challenge is that the GPS receiver and camera are not time synchronized (there is an unknown time offset between the two systems). The problem is formulated as an estimation problem to estimate the parameter vector consisting of the three camera orientation angles and the time offset. The estimation is based on the camera measurements and the discrete time GPS trajectory. The maximum likelihood (ML) estimator using the Iterated Least Squares (ILS) algorithm is developed. It can estimate the parameter vector in continuous space using discrete-time GPS information. Simulation tests are conducted on three drone trajectories. The estimation accuracy achieves the CRLB, and thus it is statistically efficient. The results are further analyzed from the point of view of real impact: the residual bias error (following the calibration) should not be significant compared to the camera measurement error (noise standard deviation). The most suitable drone trajectory is therefore recommended among the three. Its bias error is 24% of the measurement error.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115221251","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841239
Elinor S. Davies, Á. F. García-Fernández
This paper presents a Gaussian implementation of a multi-Bernoulli track-before-detect filter for multi-target tracking with superpositional sensors. The proposed filter runs independent Bernoulli filters for each potential target. At each update step, each Bernoulli filter shares its predicted measurement information with the rest of the Bernoulli filters so that they can account for the influence of this target in the likelihood. The Bernoulli filters are implemented using unscented Kalman filters. Simulation results show the benefits of the proposed algorithm.
{"title":"A multi-Bernoulli Gaussian filter for track-before-detect with superpositional sensors","authors":"Elinor S. Davies, Á. F. García-Fernández","doi":"10.23919/fusion49751.2022.9841239","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841239","url":null,"abstract":"This paper presents a Gaussian implementation of a multi-Bernoulli track-before-detect filter for multi-target tracking with superpositional sensors. The proposed filter runs independent Bernoulli filters for each potential target. At each update step, each Bernoulli filter shares its predicted measurement information with the rest of the Bernoulli filters so that they can account for the influence of this target in the likelihood. The Bernoulli filters are implemented using unscented Kalman filters. Simulation results show the benefits of the proposed algorithm.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127015059","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841375
Simon Bachhuber, Daniel Weber, I. Weygers, T. Seel
Inertial measurement units are widely used for motion tracking of kinematic chains in numerous applications. While magnetometer-free sensor fusion enables reliably high accuracy in indoor environments and near magnetic disturbances, the use of sparse sensor setups would yield additional advantages in cost, effort, and usability. However, it is unclear which sparse sensor setups can be used to track which motions of which kinematic chains, since observability of the underlying nonlinear dynamics is barely understood to date. We propose a method that utilizes recurrent neural networks (RNNs) and automatically generated training data to assess the observability of the relative pose of kinematic chains in sparse inertial motion tracking (IMT) systems. We apply this method to a range of double-hinge-joint systems that perform fully-exciting random motion. Results show how the degree of observability depends on the kinematic structure and that RNN-based observers can achieve small tracking errors in a large range of sparse and magnetometer-free setups. The proposed methods enable systematic assessment of observability properties in complex nonlinear dynamics and represent a key step toward enabling reliably accurate and non-restrictive IMT solutions.
{"title":"RNN-based Observability Analysis for Magnetometer-Free Sparse Inertial Motion Tracking","authors":"Simon Bachhuber, Daniel Weber, I. Weygers, T. Seel","doi":"10.23919/fusion49751.2022.9841375","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841375","url":null,"abstract":"Inertial measurement units are widely used for motion tracking of kinematic chains in numerous applications. While magnetometer-free sensor fusion enables reliably high accuracy in indoor environments and near magnetic disturbances, the use of sparse sensor setups would yield additional advantages in cost, effort, and usability. However, it is unclear which sparse sensor setups can be used to track which motions of which kinematic chains, since observability of the underlying nonlinear dynamics is barely understood to date. We propose a method that utilizes recurrent neural networks (RNNs) and automatically generated training data to assess the observability of the relative pose of kinematic chains in sparse inertial motion tracking (IMT) systems. We apply this method to a range of double-hinge-joint systems that perform fully-exciting random motion. Results show how the degree of observability depends on the kinematic structure and that RNN-based observers can achieve small tracking errors in a large range of sparse and magnetometer-free setups. The proposed methods enable systematic assessment of observability properties in complex nonlinear dynamics and represent a key step toward enabling reliably accurate and non-restrictive IMT solutions.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127301596","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841236
Juliane Regina de Oliveira, C. Dias, Eduardo Rodrigues de Lima, L. M. Almeida, Lucas Wanner
Power outages pose meaningful economic and social impacts on communities around the world. However, society's increasing reliance on electricity reduces the tolerance for power outages and consequently highlights the need to enhance the power grid resilience against natural hazards. For example, power lines based on cable-stayed towers must take special care to avoid cable loosening or foundation settlement, leading to tower collapse and cascading power failures. Our work uses a data fusion strategy to improve the inference quality of faulty or noisy sensors in remote monitoring. Machine Learning (ML) models based on Feedforward Neural Networks (FNN) and Principal Component Analysis (PCA) are used to predict expected values based on correlated sensor data. Our experiments compare the data fusion approaches with the ground truth values of inclination and cable tension. We show that the strategies with PCA and FNN and only with FNN reduced the Mean Absolute Percentage Error (MAPE) for cable tension estimation by 54% and 65% on average, respectively, with a corresponding error reduction of 37% and 54% on average for tower displacement estimation.
{"title":"Data fusion strategies for improving resilience to sensor noise in cable-stayed tower monitoring","authors":"Juliane Regina de Oliveira, C. Dias, Eduardo Rodrigues de Lima, L. M. Almeida, Lucas Wanner","doi":"10.23919/fusion49751.2022.9841236","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841236","url":null,"abstract":"Power outages pose meaningful economic and social impacts on communities around the world. However, society's increasing reliance on electricity reduces the tolerance for power outages and consequently highlights the need to enhance the power grid resilience against natural hazards. For example, power lines based on cable-stayed towers must take special care to avoid cable loosening or foundation settlement, leading to tower collapse and cascading power failures. Our work uses a data fusion strategy to improve the inference quality of faulty or noisy sensors in remote monitoring. Machine Learning (ML) models based on Feedforward Neural Networks (FNN) and Principal Component Analysis (PCA) are used to predict expected values based on correlated sensor data. Our experiments compare the data fusion approaches with the ground truth values of inclination and cable tension. We show that the strategies with PCA and FNN and only with FNN reduced the Mean Absolute Percentage Error (MAPE) for cable tension estimation by 54% and 65% on average, respectively, with a corresponding error reduction of 37% and 54% on average for tower displacement estimation.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114342579","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841364
B. Noack, Clemens Öhl, U. Hanebeck
In networked estimation architectures, event-based sensing and communication can contribute to a more efficient resource allocation in general, and improved utilization of communication resources, in particular. In order to tap the full potential of event-based scheduling, the design of transmission triggers and estimators need to be closely coupled while two directions are promising: First, the remote estimator can exploit the absence of transmissions and translate it into implicit information about the sensor data. Second, an intelligent trigger mechanism at the sensor that predicts future sensor readings can decrease transmission rates while rendering the implicit information more valuable. Such an intelligent trigger has been developed in a recent paper based on a Finite Impulse Response filter, which requires the sensor to transmit an additional estimate alongside the measurement. In the present paper, the communication demand is further reduced by only transmitting the estimate. The remote estimator exploits correlations to incorporate the received information. In doing so, the estimation quality is also improved, which is confirmed by simulations.
{"title":"Event-Based Kalman Filtering Exploiting Correlated Trigger Information","authors":"B. Noack, Clemens Öhl, U. Hanebeck","doi":"10.23919/fusion49751.2022.9841364","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841364","url":null,"abstract":"In networked estimation architectures, event-based sensing and communication can contribute to a more efficient resource allocation in general, and improved utilization of communication resources, in particular. In order to tap the full potential of event-based scheduling, the design of transmission triggers and estimators need to be closely coupled while two directions are promising: First, the remote estimator can exploit the absence of transmissions and translate it into implicit information about the sensor data. Second, an intelligent trigger mechanism at the sensor that predicts future sensor readings can decrease transmission rates while rendering the implicit information more valuable. Such an intelligent trigger has been developed in a recent paper based on a Finite Impulse Response filter, which requires the sensor to transmit an additional estimate alongside the measurement. In the present paper, the communication demand is further reduced by only transmitting the estimate. The remote estimator exploits correlations to incorporate the received information. In doing so, the estimation quality is also improved, which is confirmed by simulations.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125481440","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841260
N. Rao, Chris Y. T. Ma, Fei He
We consider two streams of data or measurements with disparate qualities and time resolutions that need to be classified. The first stream consists of higher quality data at a coarser time resolution, and the other consists of lower quality data at a finer time resolution. We present a fuser-switch method that fuses the set of classifiers of each stream separately and switches between them. We show that this method provides classification decisions at a finer time resolution with superior detection and false alarm probabilities compared to individual classifiers, under the statistical independence and time resolution ratio conditions. When classifiers are trained using machine learning methods, we show that this superior performance is guaranteed with a confidence probability specified by the classifiers' generalization equations. We use these results to provide analytical foundations for previous practical results that achieved significant performance improvements in classifying Pu/Np target dissolution events at a radiochemical processing facility.
{"title":"Classification and Fusion of Two Disparate Data Streams and Nuclear Dissolutions Application","authors":"N. Rao, Chris Y. T. Ma, Fei He","doi":"10.23919/fusion49751.2022.9841260","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841260","url":null,"abstract":"We consider two streams of data or measurements with disparate qualities and time resolutions that need to be classified. The first stream consists of higher quality data at a coarser time resolution, and the other consists of lower quality data at a finer time resolution. We present a fuser-switch method that fuses the set of classifiers of each stream separately and switches between them. We show that this method provides classification decisions at a finer time resolution with superior detection and false alarm probabilities compared to individual classifiers, under the statistical independence and time resolution ratio conditions. When classifiers are trained using machine learning methods, we show that this superior performance is guaranteed with a confidence probability specified by the classifiers' generalization equations. We use these results to provide analytical foundations for previous practical results that achieved significant performance improvements in classifying Pu/Np target dissolution events at a radiochemical processing facility.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127096915","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}
Fusion of high-level symbolic reasoning with lower level signal-based reasoning has attracted significant attention. We propose an architecture that integrates the high-level symbolic domain knowledge using a hierarchical planner with a lower level reinforcement learner that uses hybrid data (structured and unstructured). We introduce a novel neuro-symbolic system, Hybrid Deep RePReL that achieves the best of both worlds-the generalization ability of the planner with the effective learning ability of deep RL. Our results in two domains demonstrate the superiority of our approach in terms of sample efficiency as well as generalization to increased set of objects.
{"title":"Hybrid Deep RePReL: Integrating Relational Planning and Reinforcement Learning for Information Fusion","authors":"Harsha Kokel, Nikhilesh Prabhakar, Balaraman Ravindran, Erik Blasch, Prasad Tadepalli, Sriraam Natarajan","doi":"10.23919/fusion49751.2022.9841246","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841246","url":null,"abstract":"Fusion of high-level symbolic reasoning with lower level signal-based reasoning has attracted significant attention. We propose an architecture that integrates the high-level symbolic domain knowledge using a hierarchical planner with a lower level reinforcement learner that uses hybrid data (structured and unstructured). We introduce a novel neuro-symbolic system, Hybrid Deep RePReL that achieves the best of both worlds-the generalization ability of the planner with the effective learning ability of deep RL. Our results in two domains demonstrate the superiority of our approach in terms of sample efficiency as well as generalization to increased set of objects.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130234507","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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