Randomized controlled trials (RCT) are the gold standards for evaluating the efficacy and safety of therapeutic interventions in human subjects. In addition to the pre-specified endpoints, trial participants' experience reveals the time course of the intervention. Few analytical tools exist to summarize and visualize the individual experience of trial participants. Visual analytics allows integrative examination of temporal event patterns of patient experience, thus generating insights for better care decisions. Towards this end, we introduce TrialView, an information system that combines graph artificial intelligence (AI) and visual analytics to enhance the dissemination of trial data. TrialView offers four distinct yet interconnected views: Individual, Cohort, Progression, and Statistics, enabling an interactive exploration of individual and group-level data. The TrialView system is a general-purpose analytical tool for a broad class of clinical trials. The system is powered by graph AI, knowledge-guided clustering, explanatory modeling, and graph-based agglomeration algorithms. We demonstrate the system's effectiveness in analyzing temporal event data through a case study.
{"title":"TrialView: An AI-powered Visual Analytics System for Temporal Event Data in Clinical Trials.","authors":"Zuotian Li, Xiang Liu, Zelei Cheng, Yingjie Chen, Wanzhu Tu, Jing Su","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Randomized controlled trials (RCT) are the gold standards for evaluating the efficacy and safety of therapeutic interventions in human subjects. In addition to the pre-specified endpoints, trial participants' experience reveals the time course of the intervention. Few analytical tools exist to summarize and visualize the individual experience of trial participants. Visual analytics allows integrative examination of temporal event patterns of patient experience, thus generating insights for better care decisions. Towards this end, we introduce TrialView, an information system that combines graph artificial intelligence (AI) and visual analytics to enhance the dissemination of trial data. TrialView offers four distinct yet interconnected views: Individual, Cohort, Progression, and Statistics, enabling an interactive exploration of individual and group-level data. The TrialView system is a general-purpose analytical tool for a broad class of clinical trials. The system is powered by graph AI, knowledge-guided clustering, explanatory modeling, and graph-based agglomeration algorithms. We demonstrate the system's effectiveness in analyzing temporal event data through a case study.</p>","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"2024 ","pages":"1169-1178"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11052597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140873957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tiffany R Glynn, Simran S Khanna, Mohammad Adrian Hasdianda, Jeremiah Tom, Krishna Ventakasubramanian, Arlen Dumas, Conall O'Cleirigh, Charlotte E Goldfine, Peter R Chai
For marginalized populations with ongoing HIV epidemics, alternative methods are needed for understanding the complexities of HIV risk and delivering prevention interventions. Due to lack of engagement in ambulatory care, such groups have high utilization of drop-in care. Therefore, emergency departments represent a location with those at highest risk for HIV and in highest need of novel prevention methods. Digital phenotyping via data collected from smartphones and other wearable sensors could provide the innovative vehicle for examining complex HIV risk and assist in delivering personalized prevention interventions. However, there is paucity in exploring if such methods are an option. This study aimed to fill this gap via a cross-sectional psychosocial assessment with a sample of N=85 emergency department patients with HIV risk. Findings demonstrate that although potentially feasible, acceptability of digital phenotyping is questionable. Technology-assisted HIV prevention needs to be designed with the target community and address key ethical considerations.
{"title":"Informing Acceptability and Feasibility of Digital Phenotyping for Personalized HIV Prevention among Marginalized Populations Presenting to the Emergency Department.","authors":"Tiffany R Glynn, Simran S Khanna, Mohammad Adrian Hasdianda, Jeremiah Tom, Krishna Ventakasubramanian, Arlen Dumas, Conall O'Cleirigh, Charlotte E Goldfine, Peter R Chai","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>For marginalized populations with ongoing HIV epidemics, alternative methods are needed for understanding the complexities of HIV risk and delivering prevention interventions. Due to lack of engagement in ambulatory care, such groups have high utilization of drop-in care. Therefore, emergency departments represent a location with those at highest risk for HIV and in highest need of novel prevention methods. Digital phenotyping via data collected from smartphones and other wearable sensors could provide the innovative vehicle for examining complex HIV risk and assist in delivering personalized prevention interventions. However, there is paucity in exploring if such methods are an option. This study aimed to fill this gap via a cross-sectional psychosocial assessment with a sample of N=85 emergency department patients with HIV risk. Findings demonstrate that although potentially feasible, acceptability of digital phenotyping is questionable. Technology-assisted HIV prevention needs to be designed with the target community and address key ethical considerations.</p>","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"57 ","pages":"3192-3200"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10774708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139405603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georgia R Goodman, T Chris Carnes, Hannah Albrechta, Pamela Alpert, Joanne Hokayem, Charlotte Goldfine, Jasper S Lee, Edward W Boyer, Rochelle Rosen, Kenneth H Mayer, Conall O'Cleirigh, Peter R Chai
Once-daily oral pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, but its efficacy is dependent on adherence, which can be challenging for men who have sex with men (MSM) with substance use. Digital pill systems (DPS) represent a novel tool for directly measuring adherence through ingestible radiofrequency sensors that confirm ingestions in real-time. We examined operational challenges across two studies involving DPS to measure PrEP adherence. While most participants successfully operated the system, a number of technological and sociobehavioral challenges requiring intervention were identified across both studies. Technological issues were both system- and participant-related, and were primarily addressed with technical updates and participant re-education, while sociobehavioral issues, including health and housing changes and issues with technology access, warranted innovative solutions. Future research leveraging DPS technology should develop robust supportive infrastructure and mitigation procedures to promptly identify and resolve operational issues to optimize the potential benefits of DPS use.
{"title":"Real-World Implementation Challenges Associated with a Digital Pill System to Measure Adherence to HIV Pre-Exposure Prophylaxis from Two Studies of Men Who Have Sex With Men.","authors":"Georgia R Goodman, T Chris Carnes, Hannah Albrechta, Pamela Alpert, Joanne Hokayem, Charlotte Goldfine, Jasper S Lee, Edward W Boyer, Rochelle Rosen, Kenneth H Mayer, Conall O'Cleirigh, Peter R Chai","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Once-daily oral pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, but its efficacy is dependent on adherence, which can be challenging for men who have sex with men (MSM) with substance use. Digital pill systems (DPS) represent a novel tool for directly measuring adherence through ingestible radiofrequency sensors that confirm ingestions in real-time. We examined operational challenges across two studies involving DPS to measure PrEP adherence. While most participants successfully operated the system, a number of technological and sociobehavioral challenges requiring intervention were identified across both studies. Technological issues were both system- and participant-related, and were primarily addressed with technical updates and participant re-education, while sociobehavioral issues, including health and housing changes and issues with technology access, warranted innovative solutions. Future research leveraging DPS technology should develop robust supportive infrastructure and mitigation procedures to promptly identify and resolve operational issues to optimize the potential benefits of DPS use.</p>","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"57 ","pages":"3211-3221"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10783042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139426221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Out with the Old, In with the New: Examining National Cybersecurity Strategy Changes over Time","authors":"W. Cram, Jonathan Yuan","doi":"10.24251/hicss.2022.284","DOIUrl":"https://doi.org/10.24251/hicss.2022.284","url":null,"abstract":"","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"10 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76016593","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}
Museums in modern society serve to a broader public than their early predecessors. In response to such transition, many art museums now open digital exhibitions to provide immersive experience and maximize user interaction. This paper focuses on two such features – animated image and storytelling description – and their effect on museum visitors’ immersive experience and willingness-to-pay price premium (WTP). Our results indicate that animated images and storytelling description not only enhance immersion and WTP but also are more effective when adopted together. This paper contributes to both IS literature and museum industry by providing comprehensive understandings of how digital exhibition features enhance museum visitors’ immersive experience and purchase intention.
{"title":"The Roles of Digital Exhibition in Enhancing Immersive Experience and Purchase Intention","authors":"S. Yoon, Jai-Yeol Son","doi":"10.24251/HICSS.2021.545","DOIUrl":"https://doi.org/10.24251/HICSS.2021.545","url":null,"abstract":"Museums in modern society serve to a broader public than their early predecessors. In response to such transition, many art museums now open digital exhibitions to provide immersive experience and maximize user interaction. This paper focuses on two such features – animated image and storytelling description – and their effect on museum visitors’ immersive experience and willingness-to-pay price premium (WTP). Our results indicate that animated images and storytelling description not only enhance immersion and WTP but also are more effective when adopted together. This paper contributes to both IS literature and museum industry by providing comprehensive understandings of how digital exhibition features enhance museum visitors’ immersive experience and purchase intention.","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"19 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72944428","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}
AI solutions can significantly leverage open government data (OGD) ecosystems in public governance. For that, it is important to design effective and transparent governance mechanisms that create value in an OGD ecosystem through AI solutions. This article develops a conceptual model for a systematic design of an OGD governance model, which adopts a platform governance approach and integrates the governance needs derived from the use of AI. The purpose of the conceptual model is to systematically identify and analyze the interrelationships among multiple change factors on OGD governance design and to project available AI-based solutions for the OGD ecosystem by assessing the managerial, organizational, legal, technological, moral, and institutional variances. The proposed ‘6-step model’ suggests that an AI-compatible OGD ecosystem design requires (i) identifying contingencies, (ii) identifying data prosumers, (iii) assigning data governance roles, (iv) identifying design values, (v) designing the governance of AI, and (vi) designing the governance by AI. Through the recursive and reflexive analysis of each step, policymakers and system designers can develop reliable strategies in leveraging AI solutions for the use of OGD in public governance.
{"title":"Designing an AI compatible open government data ecosystem for public governance","authors":"E. Tan","doi":"10.24251/hicss.2022.291","DOIUrl":"https://doi.org/10.24251/hicss.2022.291","url":null,"abstract":"AI solutions can significantly leverage open government data (OGD) ecosystems in public governance. For that, it is important to design effective and transparent governance mechanisms that create value in an OGD ecosystem through AI solutions. This article develops a conceptual model for a systematic design of an OGD governance model, which adopts a platform governance approach and integrates the governance needs derived from the use of AI. The purpose of the conceptual model is to systematically identify and analyze the interrelationships among multiple change factors on OGD governance design and to project available AI-based solutions for the OGD ecosystem by assessing the managerial, organizational, legal, technological, moral, and institutional variances. The proposed ‘6-step model’ suggests that an AI-compatible OGD ecosystem design requires (i) identifying contingencies, (ii) identifying data prosumers, (iii) assigning data governance roles, (iv) identifying design values, (v) designing the governance of AI, and (vi) designing the governance by AI. Through the recursive and reflexive analysis of each step, policymakers and system designers can develop reliable strategies in leveraging AI solutions for the use of OGD in public governance.","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"112 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82435202","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 : 2023-03-09DOI: 10.1080/17512786.2023.2187862
P. Walters
{"title":"Pushing for Social Change: How Collaborations Are Recalibrating the Journalistic Mission","authors":"P. Walters","doi":"10.1080/17512786.2023.2187862","DOIUrl":"https://doi.org/10.1080/17512786.2023.2187862","url":null,"abstract":"","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"13 1","pages":"2380-2389"},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78411367","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 a two-player network inspection game, in which a defender allocates sensors with potentially heterogeneous detection capabilities in order to detect multiple attacks caused by a strategic attacker. The objective of the defender (resp. attacker) is to minimize (resp. maximize) the expected number of undetected attacks by selecting a potentially randomized inspection (resp. attack) strategy. We analytically characterize Nash equilibria of this large-scale zero-sum game when every vulnerable network component can be monitored from a unique sensor location. We then leverage our equilibrium analysis to design a heuristic solution approach based on minimum set covers for computing inspection strategies in general. Our computational results on a benchmark cyber-physical distribution network illustrate the performance and computational tractability of our solution approach.
{"title":"Network Inspection Using Heterogeneous Sensors for Detecting Strategic Attacks","authors":"Bobak Mccann, Mathieu Dahan","doi":"10.24251/hicss.2022.822","DOIUrl":"https://doi.org/10.24251/hicss.2022.822","url":null,"abstract":"We consider a two-player network inspection game, in which a defender allocates sensors with potentially heterogeneous detection capabilities in order to detect multiple attacks caused by a strategic attacker. The objective of the defender (resp. attacker) is to minimize (resp. maximize) the expected number of undetected attacks by selecting a potentially randomized inspection (resp. attack) strategy. We analytically characterize Nash equilibria of this large-scale zero-sum game when every vulnerable network component can be monitored from a unique sensor location. We then leverage our equilibrium analysis to design a heuristic solution approach based on minimum set covers for computing inspection strategies in general. Our computational results on a benchmark cyber-physical distribution network illustrate the performance and computational tractability of our solution approach.","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"90 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77952313","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 : 2023-02-02DOI: 10.48550/arXiv.2302.00967
Yi Xu, Silverio Mart'inez-Fern'andez, Matias Martinez, Xavier Franch
The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The increasing availability of high computational power environments allows the creation of deeper and more complex models. However, the computations needed to train such models entail a large carbon footprint. In this work, we study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO$_2$ emissions produced during training by means of an empirical study using Deep Convolutional Neural Networks. Concretely, we study: (i) the impact of the architecture and the location where the computations are hosted on the energy consumption and emissions produced; (ii) the trade-off between accuracy and energy efficiency; and (iii) the difference on the method of measurement of the energy consumed using software-based and hardware-based tools.
{"title":"Energy Efficiency of Training Neural Network Architectures: An Empirical Study","authors":"Yi Xu, Silverio Mart'inez-Fern'andez, Matias Martinez, Xavier Franch","doi":"10.48550/arXiv.2302.00967","DOIUrl":"https://doi.org/10.48550/arXiv.2302.00967","url":null,"abstract":"The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The increasing availability of high computational power environments allows the creation of deeper and more complex models. However, the computations needed to train such models entail a large carbon footprint. In this work, we study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO$_2$ emissions produced during training by means of an empirical study using Deep Convolutional Neural Networks. Concretely, we study: (i) the impact of the architecture and the location where the computations are hosted on the energy consumption and emissions produced; (ii) the trade-off between accuracy and energy efficiency; and (iii) the difference on the method of measurement of the energy consumed using software-based and hardware-based tools.","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"74 1","pages":"781-790"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79602803","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 : 2023-01-25DOI: 10.48550/arXiv.2301.10681
Thorsten Wittkopp, Dominik Scheinert, Philipp Wiesner, Alexander Acker, O. Kao
Due to the complexity of modern IT services, failures can be manifold, occur at any stage, and are hard to detect. For this reason, anomaly detection applied to monitoring data such as logs allows gaining relevant insights to improve IT services steadily and eradicate failures. However, existing anomaly detection methods that provide high accuracy often rely on labeled training data, which are time-consuming to obtain in practice. Therefore, we propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows provided by monitoring systems instead of labeled data. Our attention-based model uses a novel objective function for weak supervision deep learning that accounts for imbalanced data and applies an iterative learning strategy for positive and unknown samples (PU learning) to identify anomalous logs. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets and detects anomalous log messages with an F1-score of more than 0.99 even within imprecise failure time windows.
{"title":"PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning","authors":"Thorsten Wittkopp, Dominik Scheinert, Philipp Wiesner, Alexander Acker, O. Kao","doi":"10.48550/arXiv.2301.10681","DOIUrl":"https://doi.org/10.48550/arXiv.2301.10681","url":null,"abstract":"Due to the complexity of modern IT services, failures can be manifold, occur at any stage, and are hard to detect. For this reason, anomaly detection applied to monitoring data such as logs allows gaining relevant insights to improve IT services steadily and eradicate failures. However, existing anomaly detection methods that provide high accuracy often rely on labeled training data, which are time-consuming to obtain in practice. Therefore, we propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows provided by monitoring systems instead of labeled data. Our attention-based model uses a novel objective function for weak supervision deep learning that accounts for imbalanced data and applies an iterative learning strategy for positive and unknown samples (PU learning) to identify anomalous logs. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets and detects anomalous log messages with an F1-score of more than 0.99 even within imprecise failure time windows.","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"5 1","pages":"1376-1385"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74564368","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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