Pub Date : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00051
L. Cerný
Systems implementing safety functions are becoming more complex, which is also related to their communication and perception capabilities in an environment. Such systems, primarily seen in mobility, become more susceptible to failures in complex decision-making situations that are difficult to uncover. This paper presents an idea formed in a PhD topic on validating and verifying the system specified by formal logic models. We aim to do so by using automatically generated test scenarios including edge situations (as generalizations of edge cases) invoked by an environment in a simulation tool.
{"title":"Towards automatic validation of composite heterogeneous systems in edge situations","authors":"L. Cerný","doi":"10.1109/ISSREW55968.2022.00051","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00051","url":null,"abstract":"Systems implementing safety functions are becoming more complex, which is also related to their communication and perception capabilities in an environment. Such systems, primarily seen in mobility, become more susceptible to failures in complex decision-making situations that are difficult to uncover. This paper presents an idea formed in a PhD topic on validating and verifying the system specified by formal logic models. We aim to do so by using automatically generated test scenarios including edge situations (as generalizations of edge cases) invoked by an environment in a simulation tool.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127923625","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00077
Bohan Zhang, Yafan Huang, Rachael Chen, Guanpeng Li
This paper proposes D2MON, a data-driven real-time safety monitor, to detect and mitigate safety violations of an autonomous vehicle (AV). The key insight is that traffic situations that lead to AV safety violations fall into patterns and can be identified by learning from existing safety violations. Our approach is to use machine learning techniques to model the traffic behaviors that result in safety violations and detect their symptoms in advance before the actual crashes happen. If D2MoN detects surroundings as dangerous, it will take safety actions to mitigate the safety violations so that the AV remains safe in the evolving traffic environment. Our steps are twofold: (1) We use software fuzzing and data augmentation techniques to generate efficient safety violation data for training our ML model. (2) We deploy the model as a plug-and-play module to the AV software, detecting and mitigating safety violations of the AV in runtime. Our evaluation demonstrates our proposed technique is effective in reducing over 99% of safety violations in an industry-level autonomous driving system, Baidu Apollo.
{"title":"D2MoN: Detecting and Mitigating Real-Time Safety Violations in Autonomous Driving Systems","authors":"Bohan Zhang, Yafan Huang, Rachael Chen, Guanpeng Li","doi":"10.1109/ISSREW55968.2022.00077","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00077","url":null,"abstract":"This paper proposes D2MON, a data-driven real-time safety monitor, to detect and mitigate safety violations of an autonomous vehicle (AV). The key insight is that traffic situations that lead to AV safety violations fall into patterns and can be identified by learning from existing safety violations. Our approach is to use machine learning techniques to model the traffic behaviors that result in safety violations and detect their symptoms in advance before the actual crashes happen. If D2MoN detects surroundings as dangerous, it will take safety actions to mitigate the safety violations so that the AV remains safe in the evolving traffic environment. Our steps are twofold: (1) We use software fuzzing and data augmentation techniques to generate efficient safety violation data for training our ML model. (2) We deploy the model as a plug-and-play module to the AV software, detecting and mitigating safety violations of the AV in runtime. Our evaluation demonstrates our proposed technique is effective in reducing over 99% of safety violations in an industry-level autonomous driving system, Baidu Apollo.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125746889","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00032
Jung-Hoon Kim, Young-Sik Lee
A soft error in flash-based storage might impair a host system. For instance, if the soft error infiltrates the storage mapping function, the host system could experience severe operation failures, such as data corruption or a drive freeze. To harden the storage against soft errors, we propose a novel page-mapping consistency checker (PCK) method implemented with a lightweight redundancy. Our PCK exploits a small page tracing table written previously and only performs mapping-related functions again with the time redundant. Then, with that redundancy result, the storage detects page mapping corruption and finally recovers it. Consequently, the flash-based storage keeps the page-mapping consistency and improves the host system's reliability.
{"title":"A Page-mapping Consistency Protecting Method for Soft Error Damage in Flash-based Storage","authors":"Jung-Hoon Kim, Young-Sik Lee","doi":"10.1109/ISSREW55968.2022.00032","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00032","url":null,"abstract":"A soft error in flash-based storage might impair a host system. For instance, if the soft error infiltrates the storage mapping function, the host system could experience severe operation failures, such as data corruption or a drive freeze. To harden the storage against soft errors, we propose a novel page-mapping consistency checker (PCK) method implemented with a lightweight redundancy. Our PCK exploits a small page tracing table written previously and only performs mapping-related functions again with the time redundant. Then, with that redundancy result, the storage detects page mapping corruption and finally recovers it. Consequently, the flash-based storage keeps the page-mapping consistency and improves the host system's reliability.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133549009","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00076
Tianyu Li, Xiuwen Lu, Hui Xu
This paper studies the problem of automated test case generation for online coding test, i.e., given an input specification in natural language, how can we generate test cases automatically to examine the correctness of the code implemented by the testee? To tackle the problem, this paper proposes an approach that first extracts noun phrases from an input specification; then it removes irrelevant noun phrases and only retains the key phrases related to input construction; by reorganizing these key phrases, it can form an information tree and generate test cases accordingly. We have evaluated our approach with two datasets from LeetCode and ACM and achieved promising results.
{"title":"Automated Test Case Generation from Input Specification in Natural Language","authors":"Tianyu Li, Xiuwen Lu, Hui Xu","doi":"10.1109/ISSREW55968.2022.00076","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00076","url":null,"abstract":"This paper studies the problem of automated test case generation for online coding test, i.e., given an input specification in natural language, how can we generate test cases automatically to examine the correctness of the code implemented by the testee? To tackle the problem, this paper proposes an approach that first extracts noun phrases from an input specification; then it removes irrelevant noun phrases and only retains the key phrases related to input construction; by reorganizing these key phrases, it can form an information tree and generate test cases accordingly. We have evaluated our approach with two datasets from LeetCode and ACM and achieved promising results.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134038850","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00057
Wenxian Zhang, Kazunori Sakamoto, H. Washizaki, Y. Fukazawa
Coverage-guided fuzzing is one of the most effective types of fuzz testing. Code coverage is an important parameter of performance evaluation of the coverage-guided fuzzing tools since normally higher coverage result means a higher chance of fault detection. To expand the overall code covered, based on previous basic block analysis, we propose a method for selecting the mutants of inputs that are able to execute some specific length of the execution path.
{"title":"Improving Fuzzing Coverage with Execution Path Length Selection","authors":"Wenxian Zhang, Kazunori Sakamoto, H. Washizaki, Y. Fukazawa","doi":"10.1109/ISSREW55968.2022.00057","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00057","url":null,"abstract":"Coverage-guided fuzzing is one of the most effective types of fuzz testing. Code coverage is an important parameter of performance evaluation of the coverage-guided fuzzing tools since normally higher coverage result means a higher chance of fault detection. To expand the overall code covered, based on previous basic block analysis, we propose a method for selecting the mutants of inputs that are able to execute some specific length of the execution path.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131067574","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00097
Shamanth Manjunath, Ethan Wescoat, Vinita Jansari, Matthew Krugh, L. Mears
Bearings are a common failure component found in roto-dynamic equipment. As a bearing fails, tell-tale signs in collected data indicate progressing damage, depending on the operating conditions and bearing failure mode. This paper classifies bearing damage under different damage levels and operating conditions for contamination failure and focuses on differentiating the collected signals between different contamination levels against the baseline data. A contaminate was measured and mixed into the bearing grease before applying it to the rolling elements. An increasing amount of contamination was mixed into the bearing grease to simulate progressing damage and failure mode. Five classifiers are used to diagnose the condition: Random Forest, Multilayer Perceptron, K-Nearest Neighbor, Decision Tree, and Naive Bayes. The algorithms are compared using four different metrics: weighted average, Precision, Recall, and F-Measure. The algorithms are trained to diagnose failures over multiple operating conditions to circumvent possible operation changes in the real world. The algorithms were trained on the training dataset, and the model was deployed on unseen test data to evaluate the performance of the classifiers. Random forest classifier provided the best classification results with an overall accuracy of 96 % for the test data.
{"title":"Classification Analysis of Bearing Contrived Dataset under Different Levels of Contamination","authors":"Shamanth Manjunath, Ethan Wescoat, Vinita Jansari, Matthew Krugh, L. Mears","doi":"10.1109/ISSREW55968.2022.00097","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00097","url":null,"abstract":"Bearings are a common failure component found in roto-dynamic equipment. As a bearing fails, tell-tale signs in collected data indicate progressing damage, depending on the operating conditions and bearing failure mode. This paper classifies bearing damage under different damage levels and operating conditions for contamination failure and focuses on differentiating the collected signals between different contamination levels against the baseline data. A contaminate was measured and mixed into the bearing grease before applying it to the rolling elements. An increasing amount of contamination was mixed into the bearing grease to simulate progressing damage and failure mode. Five classifiers are used to diagnose the condition: Random Forest, Multilayer Perceptron, K-Nearest Neighbor, Decision Tree, and Naive Bayes. The algorithms are compared using four different metrics: weighted average, Precision, Recall, and F-Measure. The algorithms are trained to diagnose failures over multiple operating conditions to circumvent possible operation changes in the real world. The algorithms were trained on the training dataset, and the model was deployed on unseen test data to evaluate the performance of the classifiers. Random forest classifier provided the best classification results with an overall accuracy of 96 % for the test data.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"63 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120926792","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00065
Soichiro Sakamoto, Keita Suzuki, K. Kono
Persistent Memory(PM) has non-volatilability and byte-addressability, and it can be used in many situations due to its high reliability and high performance. However, the persis-tent nature of PM has great impact on “rejuvenation”. Crash consistency bugs, which result in inconsistent data structures inside PM after system crashes, cannot be recovered by restarting the crashed program because the data structures in PM are not initialized with the restarts. Most of existing tools for detecting crash consistency bugs adopt static analysis that can explore a wider range of PM code regions and can detect bugs effectively, but it is hard for these tools to consider all the possible states because of the combinatorial explosion. In addition, PM programs usually have recovery code, which recovers PM data from inconsistent states, hence a crash consistency bug can be recovered to a correct state and it should not be reported as a bug. To simulate the execution of PM programs and detect crash consistency bugs dynamically, we propose PM Crash Injector, the first crash injection tool for PM programs to check the correctness of the recovery code. Like fault injection tools, PM Crash Injector injects system crashes into PM programs to cause crash consistency bugs intentionally. If the recovery code works correctly, inconsistent states in PM will be recovered, but if not, they will be left in PM regions and detected as unexpected behavior the program. PM Crash Injector has found 3 bugs in real-world PM systems and 6 manually inserted bugs in the sample programs of PMDK.
{"title":"Crash Injection to Persistent Memory for Recovery Code Validation","authors":"Soichiro Sakamoto, Keita Suzuki, K. Kono","doi":"10.1109/ISSREW55968.2022.00065","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00065","url":null,"abstract":"Persistent Memory(PM) has non-volatilability and byte-addressability, and it can be used in many situations due to its high reliability and high performance. However, the persis-tent nature of PM has great impact on “rejuvenation”. Crash consistency bugs, which result in inconsistent data structures inside PM after system crashes, cannot be recovered by restarting the crashed program because the data structures in PM are not initialized with the restarts. Most of existing tools for detecting crash consistency bugs adopt static analysis that can explore a wider range of PM code regions and can detect bugs effectively, but it is hard for these tools to consider all the possible states because of the combinatorial explosion. In addition, PM programs usually have recovery code, which recovers PM data from inconsistent states, hence a crash consistency bug can be recovered to a correct state and it should not be reported as a bug. To simulate the execution of PM programs and detect crash consistency bugs dynamically, we propose PM Crash Injector, the first crash injection tool for PM programs to check the correctness of the recovery code. Like fault injection tools, PM Crash Injector injects system crashes into PM programs to cause crash consistency bugs intentionally. If the recovery code works correctly, inconsistent states in PM will be recovered, but if not, they will be left in PM regions and detected as unexpected behavior the program. PM Crash Injector has found 3 bugs in real-world PM systems and 6 manually inserted bugs in the sample programs of PMDK.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120980124","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00083
Iwo Kurzidem, Adam Misik, Philipp Schleiss, S. Burton
Safety assurance for Machine-Learning (ML) based applications such as object detection is a challenging task due to the black-box nature of many ML methods and the associated uncertainties of its output. To increase evidence in the safe behavior of such ML algorithms an explainable and/or interpretable introspective model can help to investigate the black-box prediction quality. For safety assessment this explainable model should be of reduced complexity and humanly comprehensible, so that any decision regarding safety can be traced back to known and comprehensible factors. We present an approach to create an explainable, introspective model (i.e., white-box) for a deep neural network (i.e., black-box) to determine how safety-relevant input features influence the prediction performance, in particular, for confidence and Bounding Box (BBox) regression. For this, Random Forest (RF) models are trained to predict a YOLOv5 object detector output, for specifically selected safety-relevant input features from the open context environment. The RF predicts the YOLOv5 output reliability for three safety related target variables, namely: softmax score, BBox center shift and BBox size shift. The results indicate that the RF prediction for softmax score are only reliable within certain constrains, while the RF prediction for BBox center/size shift are only reliable for small offsets.
{"title":"Safety Assessment: From Black-Box to White-Box","authors":"Iwo Kurzidem, Adam Misik, Philipp Schleiss, S. Burton","doi":"10.1109/ISSREW55968.2022.00083","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00083","url":null,"abstract":"Safety assurance for Machine-Learning (ML) based applications such as object detection is a challenging task due to the black-box nature of many ML methods and the associated uncertainties of its output. To increase evidence in the safe behavior of such ML algorithms an explainable and/or interpretable introspective model can help to investigate the black-box prediction quality. For safety assessment this explainable model should be of reduced complexity and humanly comprehensible, so that any decision regarding safety can be traced back to known and comprehensible factors. We present an approach to create an explainable, introspective model (i.e., white-box) for a deep neural network (i.e., black-box) to determine how safety-relevant input features influence the prediction performance, in particular, for confidence and Bounding Box (BBox) regression. For this, Random Forest (RF) models are trained to predict a YOLOv5 object detector output, for specifically selected safety-relevant input features from the open context environment. The RF predicts the YOLOv5 output reliability for three safety related target variables, namely: softmax score, BBox center shift and BBox size shift. The results indicate that the RF prediction for softmax score are only reliable within certain constrains, while the RF prediction for BBox center/size shift are only reliable for small offsets.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122620015","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00068
T. Hastings, Kristen R. Walcott
We are heading for a perfect storm, making open source software poisoning and next-generation supply chain attacks much easier to execute, which could have major im-plications for organizations. The widespread adoption of open source (99% of today's software utilizes open source), the ease of today's package managers, and the best practice of implementing continuous delivery for software projects provide an unprece-dented opportunity for attack. Once an adversary compromises a project, they can deploy malicious code into production under the auspicious of a software patch. Downstream projects will ingest the compromised patch, and now those projects are potentially running the malicious code. The impact could be implementing backdoors, gathering intelligence, delivering malware, or denying a service. According to Sonatype, a leading commercial software security company, these next-generation supply chain attacks have increased 430 % in the last year and there is not a good way to vet or monitor an open-source project prior to incorporating the project. In this paper, we analyzed two case studies of compromised open source components. We propose six continuous verification controls that enable organizations to make data-driven decisions and mitigate breaches, such as analyzing community metrics and project hygiene using scorecards and monitoring the boundary of the software in production. In one case study, the controls identified high levels of risk immediately even though the package is widely used and has over 7 million downloads a week. In both case studies we found that the controls could have prevented malicious actions despite the project breaches.
{"title":"Continuous Verification of Open Source Components in a World of Weak Links","authors":"T. Hastings, Kristen R. Walcott","doi":"10.1109/ISSREW55968.2022.00068","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00068","url":null,"abstract":"We are heading for a perfect storm, making open source software poisoning and next-generation supply chain attacks much easier to execute, which could have major im-plications for organizations. The widespread adoption of open source (99% of today's software utilizes open source), the ease of today's package managers, and the best practice of implementing continuous delivery for software projects provide an unprece-dented opportunity for attack. Once an adversary compromises a project, they can deploy malicious code into production under the auspicious of a software patch. Downstream projects will ingest the compromised patch, and now those projects are potentially running the malicious code. The impact could be implementing backdoors, gathering intelligence, delivering malware, or denying a service. According to Sonatype, a leading commercial software security company, these next-generation supply chain attacks have increased 430 % in the last year and there is not a good way to vet or monitor an open-source project prior to incorporating the project. In this paper, we analyzed two case studies of compromised open source components. We propose six continuous verification controls that enable organizations to make data-driven decisions and mitigate breaches, such as analyzing community metrics and project hygiene using scorecards and monitoring the boundary of the software in production. In one case study, the controls identified high levels of risk immediately even though the package is widely used and has over 7 million downloads a week. In both case studies we found that the controls could have prevented malicious actions despite the project breaches.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"297 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128608247","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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