F. Donini, A. Marcelletti, A. Morichetta, A. Polini
In inter-organizational contexts, different organizations cooperate exchanging information, to reach specific and shared objectives. The achievement of such interactions raises the need for a trusted communication environment to be used by the participants. This is a particularly relevant challenge when such interactions are specified in a peer-to-peer style, as in the case of Service Choreographies. Indeed, in such situations, the involved participants expect that all the interactions are performed abiding by the agreed specification. To support such a scenario, blockchain technology is gaining interest thanks to its security, trust, and decentralization characteristics. However, technological barriers still limit its adoption in real context due to the costly and time-consuming learning process. For this reason, we propose RESTChain, a general framework relying on blockchain technology enabling in an automatic way the interactions that take place among the participants in a service choreography. Starting from a choreography specification, the framework automatically derives a set of Mediators and a Smart Contract that coordinates the service interactions. In this way, each organization can communicate with the other services through the blockchain in a secure, auditable, and transparent manner.
{"title":"RESTChain: a Blockchain-based Mediator for REST Interactions in Service Choreographies","authors":"F. Donini, A. Marcelletti, A. Morichetta, A. Polini","doi":"10.1145/3555776.3577826","DOIUrl":"https://doi.org/10.1145/3555776.3577826","url":null,"abstract":"In inter-organizational contexts, different organizations cooperate exchanging information, to reach specific and shared objectives. The achievement of such interactions raises the need for a trusted communication environment to be used by the participants. This is a particularly relevant challenge when such interactions are specified in a peer-to-peer style, as in the case of Service Choreographies. Indeed, in such situations, the involved participants expect that all the interactions are performed abiding by the agreed specification. To support such a scenario, blockchain technology is gaining interest thanks to its security, trust, and decentralization characteristics. However, technological barriers still limit its adoption in real context due to the costly and time-consuming learning process. For this reason, we propose RESTChain, a general framework relying on blockchain technology enabling in an automatic way the interactions that take place among the participants in a service choreography. Starting from a choreography specification, the framework automatically derives a set of Mediators and a Smart Contract that coordinates the service interactions. In this way, each organization can communicate with the other services through the blockchain in a secure, auditable, and transparent manner.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74309412","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}
Barbora Buhnova, David Halasz, Danish Iqbal, Hind Bangui
Software systems across various application domains are undergoing a major shift, from static systems of systems to dynamic ecosystems characterized by largely autonomous software agents, engaging in mutual coalitions and partnerships to complete complex collaborative tasks. One of the key challenges facing software engineering along with this shift, is our preparedness to leverage the concept of mutual trust building among the dynamic system components, to support safe collaborations with (possibly malicious or misbehaving) components outside the boundaries of our control. To support safe evolution towards dynamic software ecosystems, this paper examines the current progress in the research on trust in software engineering across various application domains. To this end, it presents a survey of existing work in this area, and suggests the directions in which further research is needed. These directions include the research of social metrics supporting trust assessment, fine-grained quantification of trust-assessment results, and opening the discussion on governance mechanisms responsible for trust-score management and propagation across the integrated software ecosystems.
{"title":"Survey on Trust in Software Engineering for Autonomous Dynamic Ecosystems","authors":"Barbora Buhnova, David Halasz, Danish Iqbal, Hind Bangui","doi":"10.1145/3555776.3577702","DOIUrl":"https://doi.org/10.1145/3555776.3577702","url":null,"abstract":"Software systems across various application domains are undergoing a major shift, from static systems of systems to dynamic ecosystems characterized by largely autonomous software agents, engaging in mutual coalitions and partnerships to complete complex collaborative tasks. One of the key challenges facing software engineering along with this shift, is our preparedness to leverage the concept of mutual trust building among the dynamic system components, to support safe collaborations with (possibly malicious or misbehaving) components outside the boundaries of our control. To support safe evolution towards dynamic software ecosystems, this paper examines the current progress in the research on trust in software engineering across various application domains. To this end, it presents a survey of existing work in this area, and suggests the directions in which further research is needed. These directions include the research of social metrics supporting trust assessment, fine-grained quantification of trust-assessment results, and opening the discussion on governance mechanisms responsible for trust-score management and propagation across the integrated software ecosystems.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75159526","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}
Thomaz Pereira Da Silva Junior, Everson da Silva Flores, Vagner Santos Da Rosa, F. Borges
This paper presents a comparison of two different types of neural networks when used in the control of a hydraulic actuator. The advantages of using hydraulic actuators are pondered when facing the nonlinearities present in their model, which difficult their control difficult. The state of the art seeks several solutions, mostly in the use of neural networks. In this way, this paper addressed a study regarding the replacement of traditional sigmoidal networks by the use of wavelet networks in the representation of friction on the walls of hydraulic cylinders and reverse valve dynamics. Different architectures are tested and trained using the quickpropagation algorithm. Finally, the efficiency of the networks is compared regarding generalization for friction and reverse dynamics of the valve, as well as their use in a cascade neural control.
{"title":"Machine Learning Applied on Hydraulic Actuator Control","authors":"Thomaz Pereira Da Silva Junior, Everson da Silva Flores, Vagner Santos Da Rosa, F. Borges","doi":"10.1145/3555776.3577695","DOIUrl":"https://doi.org/10.1145/3555776.3577695","url":null,"abstract":"This paper presents a comparison of two different types of neural networks when used in the control of a hydraulic actuator. The advantages of using hydraulic actuators are pondered when facing the nonlinearities present in their model, which difficult their control difficult. The state of the art seeks several solutions, mostly in the use of neural networks. In this way, this paper addressed a study regarding the replacement of traditional sigmoidal networks by the use of wavelet networks in the representation of friction on the walls of hydraulic cylinders and reverse valve dynamics. Different architectures are tested and trained using the quickpropagation algorithm. Finally, the efficiency of the networks is compared regarding generalization for friction and reverse dynamics of the valve, as well as their use in a cascade neural control.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75346363","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}
The progress of computer-aid-diagnosis system for ultrasound breast lesions reaches tremendous success in the past few years. However, conventional deep learning-based strategies in recent developments still have challenges particularly in characterizing tumor domain in ultrasound images due to the heterogeneous and complex variations of lesions along with similar intensity exhibited in target object. To address this, this work proposes a discrete wavelet coefficient-based embeddable branch that allows to additionally propagate geometrical features of tumors in an end-to-end trainable fashion. To be elaborate, such branch priorly enforce the wavelet pooling operation to select a certain coefficient to further collect gradient information of target domain. Further, the current work also investigates two different preprocessing strategies in which the internal and external gradients of lesion areas can be emphasized within the transformation. Thus, we examine the effects of the proposed method based on different preprocessing scenarios. To verify the usefulness, GradCam projection, and the cross-validation demonstrate the connection of the proposed branch encourages the importance of target features, thus boosting the overall discrimination between lesion groups. Lastly, the proposed branch can be easily incorporated with existing deep learning-based architectures.
{"title":"Discrete Wavelet Coefficient-based Embeddable Branch for Ultrasound Breast Masses Classification","authors":"Mingue Song, Yanggon Kim","doi":"10.1145/3555776.3577727","DOIUrl":"https://doi.org/10.1145/3555776.3577727","url":null,"abstract":"The progress of computer-aid-diagnosis system for ultrasound breast lesions reaches tremendous success in the past few years. However, conventional deep learning-based strategies in recent developments still have challenges particularly in characterizing tumor domain in ultrasound images due to the heterogeneous and complex variations of lesions along with similar intensity exhibited in target object. To address this, this work proposes a discrete wavelet coefficient-based embeddable branch that allows to additionally propagate geometrical features of tumors in an end-to-end trainable fashion. To be elaborate, such branch priorly enforce the wavelet pooling operation to select a certain coefficient to further collect gradient information of target domain. Further, the current work also investigates two different preprocessing strategies in which the internal and external gradients of lesion areas can be emphasized within the transformation. Thus, we examine the effects of the proposed method based on different preprocessing scenarios. To verify the usefulness, GradCam projection, and the cross-validation demonstrate the connection of the proposed branch encourages the importance of target features, thus boosting the overall discrimination between lesion groups. Lastly, the proposed branch can be easily incorporated with existing deep learning-based architectures.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74104069","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}
Elton M. Cardoso, Regina De Paula, D. Pereira, L. Reis, R. Ribeiro
Parsing expressions grammars (PEGs) are a recognition-based formalism for language specification, which has been the subject of several research works. A PEG that succeeds or rejects every input string is said to be complete. However, checking if an arbitrary PEG is complete is an undecidable problem. In this work we propose a sound type-based termination analysis for PEGs as a type inference algorithm.
{"title":"Type-based Termination Analysis for Parsing Expression Grammars","authors":"Elton M. Cardoso, Regina De Paula, D. Pereira, L. Reis, R. Ribeiro","doi":"10.1145/3555776.3577620","DOIUrl":"https://doi.org/10.1145/3555776.3577620","url":null,"abstract":"Parsing expressions grammars (PEGs) are a recognition-based formalism for language specification, which has been the subject of several research works. A PEG that succeeds or rejects every input string is said to be complete. However, checking if an arbitrary PEG is complete is an undecidable problem. In this work we propose a sound type-based termination analysis for PEGs as a type inference algorithm.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81500528","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}
Message Queuing Telemetry Transport (MQTT) is a protocol commonly used in smart IoT applications. The protocol reduces the resource saturation but does not implement appropriate security mechanisms. There have been attempts to add security features to MQTT; however, they do not take into account the resource-constrained nature of IoT devices. The Cipher-text Policy Attribute-Based Encryption (CP-ABE) scheme provides fine-grained access to topic-related data and adequate data storage on MQTT server. In this work, we propose an Improved CP-ABE (ICP-ABE) scheme integrated with a lightweight symmetric encryption algorithm - PRESENT. The new scheme separates the roles of attribute auditing and key extraction. By using a blind key, MQTT servers verify the identity of sender nodes without knowing the sender's attributes. The PRESENT algorithm is employed in the proposed scheme in order to securely share such blind keys between clients. The efficiency of the scheme is evaluated in terms of throughput, packet delivery ratio, network delay, and execution time.
{"title":"A Lightweight Authentication and Privacy Preservation Scheme for MQTT","authors":"Sijia Tian, V. Vassilakis","doi":"10.1145/3555776.3577817","DOIUrl":"https://doi.org/10.1145/3555776.3577817","url":null,"abstract":"Message Queuing Telemetry Transport (MQTT) is a protocol commonly used in smart IoT applications. The protocol reduces the resource saturation but does not implement appropriate security mechanisms. There have been attempts to add security features to MQTT; however, they do not take into account the resource-constrained nature of IoT devices. The Cipher-text Policy Attribute-Based Encryption (CP-ABE) scheme provides fine-grained access to topic-related data and adequate data storage on MQTT server. In this work, we propose an Improved CP-ABE (ICP-ABE) scheme integrated with a lightweight symmetric encryption algorithm - PRESENT. The new scheme separates the roles of attribute auditing and key extraction. By using a blind key, MQTT servers verify the identity of sender nodes without knowing the sender's attributes. The PRESENT algorithm is employed in the proposed scheme in order to securely share such blind keys between clients. The efficiency of the scheme is evaluated in terms of throughput, packet delivery ratio, network delay, and execution time.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78336149","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}
Ajung Kim, Gwangyong Kim, Bong-hoi Kim, Jiman Hong
The Baseboard Management Controller1 (BMC) reduces the operating cost of the server because it enables remote monitoring of the server. In order to reduce the boot time of the BMC, the hibernation technique has been applied for the fast boot of the BMC. However, it is difficult to apply the existing hibernation technique to the BMC as it is because the boot time may be longer than the cold boot since memory usage is not constant for each BMC. In this paper, we propose a hybrid boot technique that selects the faster boot between cold boot and hibernation-based boot based on the proper hibernation execution periodic interval. The proposed technique can perform boot at a point where the boot time is expected to be the minimum by checking memory usage. The experimental results show that the proposed hybrid boot technique can reduce the total boot time significantly compared to cold boot.
{"title":"Hibernation Execution Interval based Hybrid Boot for Baseboard Management Controllers","authors":"Ajung Kim, Gwangyong Kim, Bong-hoi Kim, Jiman Hong","doi":"10.1145/3555776.3577729","DOIUrl":"https://doi.org/10.1145/3555776.3577729","url":null,"abstract":"The Baseboard Management Controller1 (BMC) reduces the operating cost of the server because it enables remote monitoring of the server. In order to reduce the boot time of the BMC, the hibernation technique has been applied for the fast boot of the BMC. However, it is difficult to apply the existing hibernation technique to the BMC as it is because the boot time may be longer than the cold boot since memory usage is not constant for each BMC. In this paper, we propose a hybrid boot technique that selects the faster boot between cold boot and hibernation-based boot based on the proper hibernation execution periodic interval. The proposed technique can perform boot at a point where the boot time is expected to be the minimum by checking memory usage. The experimental results show that the proposed hybrid boot technique can reduce the total boot time significantly compared to cold boot.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73301163","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}
Logical languages provide rigid formalisms for theories with varying expressive and scalable powers. In ontology engineering, it is popular to to provide a two-folded formalization of a theory; an expressive FOL formalization, and a decidable SROIQ fragment. Such a task requires a systematic and principled translation of the set of FOL formulas to achieve a maximally expressive decidable fragment. While no principled work exists for providing guidelines for the translation of FOL theories into SROIQ knowledge bases, this paper contributes with such a translation procedure.
{"title":"Translating FOL-theories into SROIQ-Tboxes","authors":"Fatima Danash, D. Ziébelin","doi":"10.1145/3555776.3577870","DOIUrl":"https://doi.org/10.1145/3555776.3577870","url":null,"abstract":"Logical languages provide rigid formalisms for theories with varying expressive and scalable powers. In ontology engineering, it is popular to to provide a two-folded formalization of a theory; an expressive FOL formalization, and a decidable SROIQ fragment. Such a task requires a systematic and principled translation of the set of FOL formulas to achieve a maximally expressive decidable fragment. While no principled work exists for providing guidelines for the translation of FOL theories into SROIQ knowledge bases, this paper contributes with such a translation procedure.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73402330","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}
Francesco Lettich, Chiara Pugliese, C. Renso, Fabio Pinelli
The massive use of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. Such data can be enriched with several semantic dimensions (or aspects), i.e., contextual and heterogeneous information captured in the surrounding environment, leading to the creation of multiple aspect trajectories (MATs). In this work, we present how the MAT-Builder system can be used for the semantic enrichment processing of movement data while being agnostic to aspects and external semantic data sources. This is achieved by integrating MAT-Builder into a methodology which encompasses three design principles and a uniform representation formalism for enriched data based on the Resource Description Framework (RDF) format. An example scenario involving the generation and querying of a dataset of MATs gives a glimpse of the possibilities that our methodology can open up.
{"title":"A general methodology for building multiple aspect trajectories","authors":"Francesco Lettich, Chiara Pugliese, C. Renso, Fabio Pinelli","doi":"10.1145/3555776.3577832","DOIUrl":"https://doi.org/10.1145/3555776.3577832","url":null,"abstract":"The massive use of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. Such data can be enriched with several semantic dimensions (or aspects), i.e., contextual and heterogeneous information captured in the surrounding environment, leading to the creation of multiple aspect trajectories (MATs). In this work, we present how the MAT-Builder system can be used for the semantic enrichment processing of movement data while being agnostic to aspects and external semantic data sources. This is achieved by integrating MAT-Builder into a methodology which encompasses three design principles and a uniform representation formalism for enriched data based on the Resource Description Framework (RDF) format. An example scenario involving the generation and querying of a dataset of MATs gives a glimpse of the possibilities that our methodology can open up.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81657082","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}
Over the last two decades, Associative Classifiers have shown competitive performance in the task of predicting class labels. Along with the performance in accuracy, associative classifiers produce human-readable predictive rules which is very helpful to understand the decision process of the classifiers. Associative classifiers from early days suffer from the limitation requiring proper threshold value setting which is dataset-specific. Recently some studies eliminated that limitation by producing statistically significant rules. Though recent models showed very competitive performance with state-of-the-art classifiers, their performance is still impacted if the feature vector of the training data is very large. An ensemble model can solve this issue by training each base learner with a subset of the feature vector. In this study, we propose an ensemble model Classification by Frequent Association Rules (CFAR) using associative classifiers as base learners. In our approach, instead of using a classical ensemble and a voting method, we rank the generated rules based on predominance among base learners and select a subset of the rules for predicting class labels. We use 10 datasets from the UCI repository to evaluate the performance of the proposed model. Our ensemble approach CFAR eliminates the limitation of high memory requirement and runtime of recent associative classifiers if training datasets have large feature vectors. Among the datasets we used, along with increasing accuracy in most cases, CFAR removes the noisy rules which enhances the interpretability of the model.
{"title":"Classification by Frequent Association Rules","authors":"Md Rayhan Kabir, Osmar Zaiane","doi":"10.1145/3555776.3577848","DOIUrl":"https://doi.org/10.1145/3555776.3577848","url":null,"abstract":"Over the last two decades, Associative Classifiers have shown competitive performance in the task of predicting class labels. Along with the performance in accuracy, associative classifiers produce human-readable predictive rules which is very helpful to understand the decision process of the classifiers. Associative classifiers from early days suffer from the limitation requiring proper threshold value setting which is dataset-specific. Recently some studies eliminated that limitation by producing statistically significant rules. Though recent models showed very competitive performance with state-of-the-art classifiers, their performance is still impacted if the feature vector of the training data is very large. An ensemble model can solve this issue by training each base learner with a subset of the feature vector. In this study, we propose an ensemble model Classification by Frequent Association Rules (CFAR) using associative classifiers as base learners. In our approach, instead of using a classical ensemble and a voting method, we rank the generated rules based on predominance among base learners and select a subset of the rules for predicting class labels. We use 10 datasets from the UCI repository to evaluate the performance of the proposed model. Our ensemble approach CFAR eliminates the limitation of high memory requirement and runtime of recent associative classifiers if training datasets have large feature vectors. Among the datasets we used, along with increasing accuracy in most cases, CFAR removes the noisy rules which enhances the interpretability of the model.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82466511","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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