Pub Date : 2023-04-17DOI: 10.1109/ITNT57377.2023.10138991
Natalya V. Pustovalova, T. Avdeenko
The paper presents the results of regression analysis of specially designed dataset. The students from the second to fourth year of the Novosibirsk State Technical University have passed testing procedure, among them 123 men and 68 women at the age from 18 to 23 years. The presented dataset contains the results of eight different tests. We designed this set of psychometric tests for implementing "Learner model". The "Learner model" is an important component of personal educational environment of a university. For implementing the "Learner model", we preprocess testing data and create regression models. The method of regression analysis allows exploring the most significant predictors affecting academic performance. As a result, the most significant predictors are "conscientiousness" and "behavior inhibition system". The same predictors are significant for exploring interaction effects with categorical predictors "modality", "style of reaction on changes", "gender". We also explored combinations of psychometric characteristics for finding their influence on academic problems. For this reason, we divided students into two categories, considering their academic performance. Then, we build a logistic regression model.
{"title":"Analysis of the Influence of Psychological Characteristics and Their Combinations on the Students' Academic Performance","authors":"Natalya V. Pustovalova, T. Avdeenko","doi":"10.1109/ITNT57377.2023.10138991","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10138991","url":null,"abstract":"The paper presents the results of regression analysis of specially designed dataset. The students from the second to fourth year of the Novosibirsk State Technical University have passed testing procedure, among them 123 men and 68 women at the age from 18 to 23 years. The presented dataset contains the results of eight different tests. We designed this set of psychometric tests for implementing \"Learner model\". The \"Learner model\" is an important component of personal educational environment of a university. For implementing the \"Learner model\", we preprocess testing data and create regression models. The method of regression analysis allows exploring the most significant predictors affecting academic performance. As a result, the most significant predictors are \"conscientiousness\" and \"behavior inhibition system\". The same predictors are significant for exploring interaction effects with categorical predictors \"modality\", \"style of reaction on changes\", \"gender\". We also explored combinations of psychometric characteristics for finding their influence on academic problems. For this reason, we divided students into two categories, considering their academic performance. Then, we build a logistic regression model.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124262894","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 : 2023-04-17DOI: 10.1109/ITNT57377.2023.10139009
A. Agafonov, Evgeniya Efimenko
The paper is devoted to the short-term travel time prediction of individual connected vehicles in a regulated road network with adaptive control of traffic lights. The estimation of the total travel time combines both the travel time along road network links, obtained by a neural network model, and the waiting time at regulated intersections. At the first stage, it is proposed to use the model based on a neural network to estimate the travel time along the road links of the transportation network. At the second stage, the phase of the traffic light is predicted using the adaptive control method. Finally, the waiting time at the intersection is calculated based on the predicted arrival time of the vehicle at the intersection and the duration of the traffic light phase. Experimental results in a microscopic simulation environment allow us to conclude that the proposed approach outperforms baseline methods in terms of the mean absolute error criterion.
{"title":"Connected Vehicles Travel Time Prediction in a Scenario with Adaptive Traffic Light Control","authors":"A. Agafonov, Evgeniya Efimenko","doi":"10.1109/ITNT57377.2023.10139009","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139009","url":null,"abstract":"The paper is devoted to the short-term travel time prediction of individual connected vehicles in a regulated road network with adaptive control of traffic lights. The estimation of the total travel time combines both the travel time along road network links, obtained by a neural network model, and the waiting time at regulated intersections. At the first stage, it is proposed to use the model based on a neural network to estimate the travel time along the road links of the transportation network. At the second stage, the phase of the traffic light is predicted using the adaptive control method. Finally, the waiting time at the intersection is calculated based on the predicted arrival time of the vehicle at the intersection and the duration of the traffic light phase. Experimental results in a microscopic simulation environment allow us to conclude that the proposed approach outperforms baseline methods in terms of the mean absolute error criterion.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"452 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125786780","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 : 2023-04-17DOI: 10.1109/ITNT57377.2023.10139044
Francesco Conti, O. Papini, D. Moroni, G. Pieri, M. Reggiannini, M. A. Pascali
Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.
{"title":"Analysis of sea surface temperature maps via topological machine learning","authors":"Francesco Conti, O. Papini, D. Moroni, G. Pieri, M. Reggiannini, M. A. Pascali","doi":"10.1109/ITNT57377.2023.10139044","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139044","url":null,"abstract":"Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122377294","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 : 2023-04-17DOI: 10.1109/ITNT57377.2023.10139053
A. Bavrina
A method is proposed for passive protection of a surveillance camera video from a video fragment deletion attack. The method is based on the construction of local features for the samples of two consecutive frames, followed by a multilayer neural network classification. Post-processing and calculation of statistics based on the results of the classification make it possible to decide whether a given pair of frames is sequential or a number of frames were cut between them. Experiments show the efficiency of detecting the fact of a fragment removal even from stationary scenes, when such a deletion is visually imperceptible.
{"title":"Method for Frame Removal Detection in Static Camera Surveillance Video","authors":"A. Bavrina","doi":"10.1109/ITNT57377.2023.10139053","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139053","url":null,"abstract":"A method is proposed for passive protection of a surveillance camera video from a video fragment deletion attack. The method is based on the construction of local features for the samples of two consecutive frames, followed by a multilayer neural network classification. Post-processing and calculation of statistics based on the results of the classification make it possible to decide whether a given pair of frames is sequential or a number of frames were cut between them. Experiments show the efficiency of detecting the fact of a fragment removal even from stationary scenes, when such a deletion is visually imperceptible.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126025197","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 : 2023-04-17DOI: 10.1109/ITNT57377.2023.10139124
K. Shoshina, I. Vasendina, A. Shoshin
The work describes a methodology for estimating the heat loss of a building, including the calculation of the heat loss of a building. In order to develop a methodology for estimating the heat loss of a building based on neural networks, the features of a wooden housing stock were studied. The stage of collecting images for training a neural network, the stage of training an optimal neural network for solving the problem of object detection are described. The technologies necessary to solve the problem are described.
{"title":"Development of a Methodology for Estimating the Heat Loss of Buildings based on Neural Networks","authors":"K. Shoshina, I. Vasendina, A. Shoshin","doi":"10.1109/ITNT57377.2023.10139124","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139124","url":null,"abstract":"The work describes a methodology for estimating the heat loss of a building, including the calculation of the heat loss of a building. In order to develop a methodology for estimating the heat loss of a building based on neural networks, the features of a wooden housing stock were studied. The stage of collecting images for training a neural network, the stage of training an optimal neural network for solving the problem of object detection are described. The technologies necessary to solve the problem are described.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128360847","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 : 2023-04-17DOI: 10.1109/ITNT57377.2023.10139181
Gleb O. Bondarenko
Intensive neuromonitoring at the bedside of patients with severe traumatic brain injury, cerebral stroke, and any acute cerebral insufficiency is crucial for preventing secondary ischemic and hypoxic damage. Multiple authors estimate that traumatic brain injury (TBI) is the most common cause of death and severe disability in people under the age of 35. In addition, men are 2 to 3 times more likely to suffer from TBI than women. TBI can lead to a process of secondary damage that causes long-term neurological and neuropsychiatric consequences, which is a significant public health issue globally. Some studies have demonstrated differences between normal and abnormal muscle electrical activity associated with Parkinson's disease (PD). Some methods have been developed to use electromyography (EMG) as a tool to diagnose motor symptoms associated with PD, such as stiffness, gait disturbance, and tremor. Due to changes in muscle activity caused by a lack of dopamine in the central nervous system, the EMG signal in PD patients shows a different pattern than in a normal person. The use of surface and stimulation EMG directly at the bedside in the intensive care unit during the treatment of acute (TBI, stroke, etc.) and chronic cerebral insufficiency (CCI) of various etiologies, as well as intraoperatively during neurosurgical, otolaryngological, and other interventions, is urgent but difficult due to the use of modern equipment and the length of the examination. The use of surface and stimulation EMG directly at the bedside of the ambulance team in the case of CCI of various etiologies during neurosurgical, otorhinolaryngological, and other interventions is also urgent but difficult due to the use of modern equipment and the length of the examination.
{"title":"Analysis of statistically significant indicators for the 4 types of surface electromyography","authors":"Gleb O. Bondarenko","doi":"10.1109/ITNT57377.2023.10139181","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139181","url":null,"abstract":"Intensive neuromonitoring at the bedside of patients with severe traumatic brain injury, cerebral stroke, and any acute cerebral insufficiency is crucial for preventing secondary ischemic and hypoxic damage. Multiple authors estimate that traumatic brain injury (TBI) is the most common cause of death and severe disability in people under the age of 35. In addition, men are 2 to 3 times more likely to suffer from TBI than women. TBI can lead to a process of secondary damage that causes long-term neurological and neuropsychiatric consequences, which is a significant public health issue globally. Some studies have demonstrated differences between normal and abnormal muscle electrical activity associated with Parkinson's disease (PD). Some methods have been developed to use electromyography (EMG) as a tool to diagnose motor symptoms associated with PD, such as stiffness, gait disturbance, and tremor. Due to changes in muscle activity caused by a lack of dopamine in the central nervous system, the EMG signal in PD patients shows a different pattern than in a normal person. The use of surface and stimulation EMG directly at the bedside in the intensive care unit during the treatment of acute (TBI, stroke, etc.) and chronic cerebral insufficiency (CCI) of various etiologies, as well as intraoperatively during neurosurgical, otolaryngological, and other interventions, is urgent but difficult due to the use of modern equipment and the length of the examination. The use of surface and stimulation EMG directly at the bedside of the ambulance team in the case of CCI of various etiologies during neurosurgical, otorhinolaryngological, and other interventions is also urgent but difficult due to the use of modern equipment and the length of the examination.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132056706","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 : 2023-04-17DOI: 10.1109/ITNT57377.2023.10139262
M. Nikitina
Classification of plant component on image histological sections is critical for determining non-compliance type of undeclared additiveand further action for technologist, or other responsible person. However, this task is often challenging due to the absence of professional histologists or non-compliance with the conditions of microstructural analysis and also the subjective criteria for evaluation. In this study, we propose a machine learning model that automatically classifies the plant component on images histological sections. Our model uses a convolutional neural network to identify regions of plant components, then aggregates those classifications to infer predominant and minor plant components on histological sections image. We evaluated our model on an independent set of 95 images histological sections. It achieved a kappa score of 0.525 and an agreement of 66.6% with three histologists for classifying the predominant plant component, slightly higher than the inter-histologists kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three histologists were within 95% confidence intervals of agreement.
{"title":"Evaluation of Neural Network for Automated Classification of Plant Component on Histological Section","authors":"M. Nikitina","doi":"10.1109/ITNT57377.2023.10139262","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139262","url":null,"abstract":"Classification of plant component on image histological sections is critical for determining non-compliance type of undeclared additiveand further action for technologist, or other responsible person. However, this task is often challenging due to the absence of professional histologists or non-compliance with the conditions of microstructural analysis and also the subjective criteria for evaluation. In this study, we propose a machine learning model that automatically classifies the plant component on images histological sections. Our model uses a convolutional neural network to identify regions of plant components, then aggregates those classifications to infer predominant and minor plant components on histological sections image. We evaluated our model on an independent set of 95 images histological sections. It achieved a kappa score of 0.525 and an agreement of 66.6% with three histologists for classifying the predominant plant component, slightly higher than the inter-histologists kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three histologists were within 95% confidence intervals of agreement.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116809161","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 : 2023-04-17DOI: 10.1109/itnt57377.2023.10139106
{"title":"ITNT 2023 Cover Page","authors":"","doi":"10.1109/itnt57377.2023.10139106","DOIUrl":"https://doi.org/10.1109/itnt57377.2023.10139106","url":null,"abstract":"","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131569725","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 : 2023-04-17DOI: 10.1109/ITNT57377.2023.10139242
N. Dorodnykh, A. Stolbov, Olga O. Nikolaychuk, A. Yurin
One of the trends in the development of information technologies and artificial intelligence is intelligent assistants in the form of chatbots or voice assistants, which are actively beginning to be implemented in various domains. In this paper, the modeling and software implementation of a chatbot to support technical personnel in diagnosing aircraft malfunctions are considered. The models of the dialog, elements of the knowledge base implementation, as well as an example of its operation, are described. The constructed models are considered as content ontological patterns and describe the object of the study, the malfunction, and the relationships between the signs of the malfunction and its causes. These templates are used in the design of a knowledge base, containing logical rules presented in the form of decision tables of a special type. The novelty of the proposed solution is the use as a methodological basis of the principles of model-driven development in the context of creating problem-specific virtual assistants.
{"title":"An Intelligent Assistant for Decision Support in the Case of Aircraft Troubleshooting","authors":"N. Dorodnykh, A. Stolbov, Olga O. Nikolaychuk, A. Yurin","doi":"10.1109/ITNT57377.2023.10139242","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139242","url":null,"abstract":"One of the trends in the development of information technologies and artificial intelligence is intelligent assistants in the form of chatbots or voice assistants, which are actively beginning to be implemented in various domains. In this paper, the modeling and software implementation of a chatbot to support technical personnel in diagnosing aircraft malfunctions are considered. The models of the dialog, elements of the knowledge base implementation, as well as an example of its operation, are described. The constructed models are considered as content ontological patterns and describe the object of the study, the malfunction, and the relationships between the signs of the malfunction and its causes. These templates are used in the design of a knowledge base, containing logical rules presented in the form of decision tables of a special type. The novelty of the proposed solution is the use as a methodological basis of the principles of model-driven development in the context of creating problem-specific virtual assistants.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131337377","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 : 2023-04-17DOI: 10.1109/ITNT57377.2023.10139184
Vitaly Konovalov
Colorization task consists of acquiring a full-color RGB image from grayscale image or a sketch. Article is concerned with the task of colorizing grayscale cartoon images and image sequences using neural networks. Efficiency of an existing prototype algorithm is reviewed with different modifications, as well as different combinations of loss functions. A new neural network loss function is proposed. It is based on a hypothesis that specifics of cartoons, such as clear object boundaries and color consistency within those boundaries can be used to improve colorization quality. Proposed loss function uses segmentation of cartoon images in the bilateral space, and minimizes difference between closest found segments and inside each segment, thus bringing closer predicted colors within the segment and between neighboring segments. Quantitative and qualitative experiments are conducted on efficiency as well as generalization ability of modified prototype algorithm with proposed loss function. Quantitative experiments consisted of measuring PSNR, LPIPS, MSE in Lab color space and CC, while qualitative focused on comparing temporal consistency, quality of colorization and quality of generalization.
{"title":"Method for automatic cartoon colorization","authors":"Vitaly Konovalov","doi":"10.1109/ITNT57377.2023.10139184","DOIUrl":"https://doi.org/10.1109/ITNT57377.2023.10139184","url":null,"abstract":"Colorization task consists of acquiring a full-color RGB image from grayscale image or a sketch. Article is concerned with the task of colorizing grayscale cartoon images and image sequences using neural networks. Efficiency of an existing prototype algorithm is reviewed with different modifications, as well as different combinations of loss functions. A new neural network loss function is proposed. It is based on a hypothesis that specifics of cartoons, such as clear object boundaries and color consistency within those boundaries can be used to improve colorization quality. Proposed loss function uses segmentation of cartoon images in the bilateral space, and minimizes difference between closest found segments and inside each segment, thus bringing closer predicted colors within the segment and between neighboring segments. Quantitative and qualitative experiments are conducted on efficiency as well as generalization ability of modified prototype algorithm with proposed loss function. Quantitative experiments consisted of measuring PSNR, LPIPS, MSE in Lab color space and CC, while qualitative focused on comparing temporal consistency, quality of colorization and quality of generalization.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125760127","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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