The 2022 edition of NL4AI was co-located with the 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022) and took place on November 30th in Udine, Italy. The call for papers attracted 17 submissions by 52 different authors from Italy (44), the UK (2), Algeria (4), and Germany (2). After the review process, 13 of 17 papers were accepted for publication (acceptance rate 76.47% ). In terms of topics, the contributions to the workshop span from pure NLP works to broader proposals bridging NLP with other AI applications. Among the accepted articles, we selected two that we considered the most relevant and inspiring for the Italian Natural Language Processing and Artificial Intelligence communities. The authors of these papers have been invited to extend their contribution to this volume, creating an exclusive venue to provide visibility to their egregious work.
{"title":"Special Issue NL4AI 2022: Workshop on natural language for artificial intelligence","authors":"Debora Nozza, Lucia Passaro, Marco Polignano","doi":"10.3233/ia-230057","DOIUrl":"https://doi.org/10.3233/ia-230057","url":null,"abstract":"The 2022 edition of NL4AI was co-located with the 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022) and took place on November 30th in Udine, Italy. The call for papers attracted 17 submissions by 52 different authors from Italy (44), the UK (2), Algeria (4), and Germany (2). After the review process, 13 of 17 papers were accepted for publication (acceptance rate 76.47% ). In terms of topics, the contributions to the workshop span from pure NLP works to broader proposals bridging NLP with other AI applications. Among the accepted articles, we selected two that we considered the most relevant and inspiring for the Italian Natural Language Processing and Artificial Intelligence communities. The authors of these papers have been invited to extend their contribution to this volume, creating an exclusive venue to provide visibility to their egregious work.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138970534","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}
Multimedia item characteristics are used in domains, such as recommender systems and information retrieval. In this work we distinguish two main groups of item characteristics: (i) item-centric item characteristic (ICIC) and (ii) user-centric item characteristic (UCIC). With the term ICIC we denote a characteristic of an item that (a) has roots in the item and (b) has the same value for all users, for example, the duration of a song. With the term UCIC, we denote a characteristic of an item that (a) has roots in the perception of the user from an item characteristic and (b) exhibits some variance across different users, for example, the perceived emotion of a song. We survey recent work that covers various types of UCIC, acquisition methods of UCIC, and domain usage of UCIC. We identify gaps in the research and provide guidelines for future work.
{"title":"User-centric item characteristics for personalized multimedia systems: A systematic review","authors":"Elham Motamedi, Marko Tkalcic","doi":"10.3233/ia-230039","DOIUrl":"https://doi.org/10.3233/ia-230039","url":null,"abstract":"Multimedia item characteristics are used in domains, such as recommender systems and information retrieval. In this work we distinguish two main groups of item characteristics: (i) item-centric item characteristic (ICIC) and (ii) user-centric item characteristic (UCIC). With the term ICIC we denote a characteristic of an item that (a) has roots in the item and (b) has the same value for all users, for example, the duration of a song. With the term UCIC, we denote a characteristic of an item that (a) has roots in the perception of the user from an item characteristic and (b) exhibits some variance across different users, for example, the perceived emotion of a song. We survey recent work that covers various types of UCIC, acquisition methods of UCIC, and domain usage of UCIC. We identify gaps in the research and provide guidelines for future work.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138996121","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}
Situated natural language interactions between humans and robots are strictly necessary for complex applications: communication here implies the reference to the environment shared between a user and the robot. This paper proposes a transformer-based architecture that supports the integration of spatial information (as logical representation) about a semantic map of the environment and the input utterances. The generated interpretation is a logical form of the command that makes references to the state of the world through a single end-to-end process, stimulated at each interaction by an explicit linguistic description of the environment. In this specific work, the end-to-end capability of the targeted transformer is studied in light of its multilingual applications where the robot can be queried in different natural languages. The obtained experimental results confirm the applicability of transformers to grounded human-robotic interaction, with benefits in terms of both portability of the approach across domains and effectiveness in terms of reachable accuracy. Moreover, language-specific processing chains are shown to be preferable to large-scale multilingual models for their better trade-off between accuracy and complexity. Overall, the proposed architecture outperforms previous approaches and paves the way for sustainable multilingual architectures.
{"title":"Grounding End-to-End Pre-trained architectures for Semantic Role Labeling in multiple languages","authors":"Claudiu D. Hromei, Danilo Croce, Roberto Basili","doi":"10.3233/ia-230012","DOIUrl":"https://doi.org/10.3233/ia-230012","url":null,"abstract":"Situated natural language interactions between humans and robots are strictly necessary for complex applications: communication here implies the reference to the environment shared between a user and the robot. This paper proposes a transformer-based architecture that supports the integration of spatial information (as logical representation) about a semantic map of the environment and the input utterances. The generated interpretation is a logical form of the command that makes references to the state of the world through a single end-to-end process, stimulated at each interaction by an explicit linguistic description of the environment. In this specific work, the end-to-end capability of the targeted transformer is studied in light of its multilingual applications where the robot can be queried in different natural languages. The obtained experimental results confirm the applicability of transformers to grounded human-robotic interaction, with benefits in terms of both portability of the approach across domains and effectiveness in terms of reachable accuracy. Moreover, language-specific processing chains are shown to be preferable to large-scale multilingual models for their better trade-off between accuracy and complexity. Overall, the proposed architecture outperforms previous approaches and paves the way for sustainable multilingual architectures.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312563","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}
Online misinformation is posing a serious threat for the modern society. Assessing the veracity of online information is a complex problem which nowadays is addressed by heavily relying on trained fact-checking experts. This solution is not scalable, and due to the importance of the problem the issue gained the attention of the scientific community, which proposed many based on Artificial Intelligence and Machine Learning methods. Despite the efforts made, the effectiveness of such approaches is not yet enough to allow them to be used without supervision. In this position paper, we propose a hybrid human-in-the-loop framework for fact-checking: we address the misinformation issue by relying on a combination of automatic Artificial Intelligence methods, crowdsourcing ones, and experts. We study the single components of the framework as well as their interactions, and we propose an interleaving of the different components which we believe will serve as a useful starting point for the future research towards effective and scalable fact-checking.
{"title":"Combining human intelligence and machine learning for fact-checking: Towards a hybrid human-in-the-loop framework","authors":"David La Barbera, Kevin Roitero, Stefano Mizzaro","doi":"10.3233/ia-230011","DOIUrl":"https://doi.org/10.3233/ia-230011","url":null,"abstract":"Online misinformation is posing a serious threat for the modern society. Assessing the veracity of online information is a complex problem which nowadays is addressed by heavily relying on trained fact-checking experts. This solution is not scalable, and due to the importance of the problem the issue gained the attention of the scientific community, which proposed many based on Artificial Intelligence and Machine Learning methods. Despite the efforts made, the effectiveness of such approaches is not yet enough to allow them to be used without supervision. In this position paper, we propose a hybrid human-in-the-loop framework for fact-checking: we address the misinformation issue by relying on a combination of automatic Artificial Intelligence methods, crowdsourcing ones, and experts. We study the single components of the framework as well as their interactions, and we propose an interleaving of the different components which we believe will serve as a useful starting point for the future research towards effective and scalable fact-checking.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312546","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 study the problem of online interaction in general decision making problems, where the objective is not only to find optimal strategies, but also to satisfy certain safety guarantees, expressed in terms of costs accrued. In particular, we focus on the online learning problem in which an agent has to find the optimal solution of a linear objective. Moreover, the agent has to satisfy a linear safety constraint at each round. We propose a theoretical framework to address such problems and present BAN-SOLO, a UCB-like algorithm that, in an online interaction with an unknown environment, attains sublinear regret of order O ( T ) and satisfies a safety constraint with high probability at each iteration. BAN-SOLO provides a general framework that can be applied to any setting in which estimators of the objective and the cost function are available. At its core, it relies on tools from convex duality to manage environment exploration while satisfying the safety constraint imposed by the problem. To show the applicability of our framework, we provide two game theoretical applications: normal-form games and sequential decision-making problems.
{"title":"A framework for safe decision making: A convex duality approach","authors":"Martino Bernasconi, Federico Cacciamani, Matteo Castiglioni","doi":"10.3233/ia-230008","DOIUrl":"https://doi.org/10.3233/ia-230008","url":null,"abstract":"We study the problem of online interaction in general decision making problems, where the objective is not only to find optimal strategies, but also to satisfy certain safety guarantees, expressed in terms of costs accrued. In particular, we focus on the online learning problem in which an agent has to find the optimal solution of a linear objective. Moreover, the agent has to satisfy a linear safety constraint at each round. We propose a theoretical framework to address such problems and present BAN-SOLO, a UCB-like algorithm that, in an online interaction with an unknown environment, attains sublinear regret of order O ( T ) and satisfies a safety constraint with high probability at each iteration. BAN-SOLO provides a general framework that can be applied to any setting in which estimators of the objective and the cost function are available. At its core, it relies on tools from convex duality to manage environment exploration while satisfying the safety constraint imposed by the problem. To show the applicability of our framework, we provide two game theoretical applications: normal-form games and sequential decision-making problems.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312548","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}
Representing uncertain information is crucial for modeling real world domains. This has been fully recognized both in the field of Logic Programming and of Description Logics (DLs), with the introduction of probabilistic logic languages and various probabilistic extensions of DLs respectively. Several works have considered the distribution semantics as the underlying semantics of Probabilistic Logic Programming (PLP) languages and probabilistic DLs (PDLs), and have then targeted the problem of reasoning and learning in them. This paper is a survey of inference, parameter and structure learning algorithms for PLP languages and PDLs based on the distribution semantics. A few of these algorithms are also available as web applications.
{"title":"The distribution semantics in probabilistic logic programming and probabilistic description logics: a survey","authors":"Elena Bellodi","doi":"10.3233/IA-221072","DOIUrl":"https://doi.org/10.3233/IA-221072","url":null,"abstract":"Representing uncertain information is crucial for modeling real world domains. This has been fully recognized both in the field of Logic Programming and of Description Logics (DLs), with the introduction of probabilistic logic languages and various probabilistic extensions of DLs respectively. Several works have considered the distribution semantics as the underlying semantics of Probabilistic Logic Programming (PLP) languages and probabilistic DLs (PDLs), and have then targeted the problem of reasoning and learning in them. This paper is a survey of inference, parameter and structure learning algorithms for PLP languages and PDLs based on the distribution semantics. A few of these algorithms are also available as web applications.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48873911","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}
Click fraud is the sort of deception in which traffic figures for online ads are intentionally inflated. For businesses that advertise online, click fraud may occur often, resulting in erroneous click statistics and lost funds. That is why many businesses are hesitant to advertise their products on websites and mobile apps. To market their products safely, businesses need a reliable technique for detecting click fraud. In this paper we present a stacking algorithm as a solution to this problem. The proposed method’s premise is to combine multiple learners to achieve an optimal result. The Synthetic Minority Oversampling Technique (SMOTE) with a combination of undersampling are chosen to handle the unbalanced dataset. In the first-level learners, there are four supervised Machine Learning algorithms, which are AdaBoost, Random Forest, Decision Tree and Logistic Regression. Moreover, Logistic Regression is used again as a the second-level learner. To verify the efficacy of the suggested approach, comparative tests are carried out on the public dataset available on Kaggle from China’s largest independent big data service platform TalkingData. Multiple indicators, such as Accuracy, F1 Score, ROC curve, Loss Log and AUC Score, are utilized to analyze the prediction outcomes. The findings reveal that the stacking method improves forecast accuracy while also maintaining a high level of stability.
{"title":"Click fraud prediction by stacking algorithm","authors":"N. Sahllal, E. M. Souidi","doi":"10.3233/IA-221069","DOIUrl":"https://doi.org/10.3233/IA-221069","url":null,"abstract":"Click fraud is the sort of deception in which traffic figures for online ads are intentionally inflated. For businesses that advertise online, click fraud may occur often, resulting in erroneous click statistics and lost funds. That is why many businesses are hesitant to advertise their products on websites and mobile apps. To market their products safely, businesses need a reliable technique for detecting click fraud. In this paper we present a stacking algorithm as a solution to this problem. The proposed method’s premise is to combine multiple learners to achieve an optimal result. The Synthetic Minority Oversampling Technique (SMOTE) with a combination of undersampling are chosen to handle the unbalanced dataset. In the first-level learners, there are four supervised Machine Learning algorithms, which are AdaBoost, Random Forest, Decision Tree and Logistic Regression. Moreover, Logistic Regression is used again as a the second-level learner. To verify the efficacy of the suggested approach, comparative tests are carried out on the public dataset available on Kaggle from China’s largest independent big data service platform TalkingData. Multiple indicators, such as Accuracy, F1 Score, ROC curve, Loss Log and AUC Score, are utilized to analyze the prediction outcomes. The findings reveal that the stacking method improves forecast accuracy while also maintaining a high level of stability.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43413400","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}
Bin Yu, Zhihui Dong, Hu Liu, Jianhong Ye, Daoge Wang
A cognitive-based routing algorithm is proposed. Concepts like local form and path algorithms are developed. Unlike current mainstream routing algorithms assume that all people know everything about the environment, the proposed algorithm allows people to have a complete or incomplete map knowledge and built up their own map knowledge in a piecemeal fashion. Using a hospital floor plan as the scenario, numerical experiments are conducted by assuming pedestrians to have different levels of map knowledge. Results show that reasonable routes could be frequently found even if pedestrians only have an incomplete knowledge of the network. Also pedestrians generally need to traverse more rooms if having zero or less map knowledge. Hence the proposed algorithm’s effectiveness is validated to some extent.
{"title":"A cognitive-based routing algorithm for crowd dynamics under incomplete or even incorrect map knowledge","authors":"Bin Yu, Zhihui Dong, Hu Liu, Jianhong Ye, Daoge Wang","doi":"10.3233/ia-221061","DOIUrl":"https://doi.org/10.3233/ia-221061","url":null,"abstract":"A cognitive-based routing algorithm is proposed. Concepts like local form and path algorithms are developed. Unlike current mainstream routing algorithms assume that all people know everything about the environment, the proposed algorithm allows people to have a complete or incomplete map knowledge and built up their own map knowledge in a piecemeal fashion. Using a hospital floor plan as the scenario, numerical experiments are conducted by assuming pedestrians to have different levels of map knowledge. Results show that reasonable routes could be frequently found even if pedestrians only have an incomplete knowledge of the network. Also pedestrians generally need to traverse more rooms if having zero or less map knowledge. Hence the proposed algorithm’s effectiveness is validated to some extent.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46951109","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}
V. Seidita, Angelo Maria Pio Sabella, Francesco Lanza, A. Chella
Thinking to oneself is a prerogative of man when he needs to think about or repeat what he is doing or experiencing. It is a way of processing information and setting in motion a decision-making process. When this is done aloud, there is also a chance that someone else will understand the meaning or reasons for the action. Equipping an agent with the ability to reveal the reasons for its decisions is both a way to improve human interaction and a way to improve the triggering of a decision process. In this work, we propose to use the speech act to enable a coalition of agents to exhibit inner speech capabilities to explain their behavior, but also to guide and reinforce the creation of an inner model. The BDI agent paradigm, Jason, and CArtAgO are used to give agents the ability to act in a human-like manner. The BDI reasoning cycle has been extended to include inner speech. The proposed solution continues the research path that started with the definition of a cognitive model and architecture for human-robot teaming interaction and aims to integrate the believable interaction paradigm in it.
{"title":"Agent talks about itself: an implementation using Jason, CArtAgO and Speech Acts","authors":"V. Seidita, Angelo Maria Pio Sabella, Francesco Lanza, A. Chella","doi":"10.3233/IA-230005","DOIUrl":"https://doi.org/10.3233/IA-230005","url":null,"abstract":" Thinking to oneself is a prerogative of man when he needs to think about or repeat what he is doing or experiencing. It is a way of processing information and setting in motion a decision-making process. When this is done aloud, there is also a chance that someone else will understand the meaning or reasons for the action. Equipping an agent with the ability to reveal the reasons for its decisions is both a way to improve human interaction and a way to improve the triggering of a decision process. In this work, we propose to use the speech act to enable a coalition of agents to exhibit inner speech capabilities to explain their behavior, but also to guide and reinforce the creation of an inner model. The BDI agent paradigm, Jason, and CArtAgO are used to give agents the ability to act in a human-like manner. The BDI reasoning cycle has been extended to include inner speech. The proposed solution continues the research path that started with the definition of a cognitive model and architecture for human-robot teaming interaction and aims to integrate the believable interaction paradigm in it.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44462824","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}
Andrea Rafanelli, S. Costantini, Giovanni De Gasperis
This paper shows the capabilities offered by an integrated neural-logic multi-agent system (MAS). Our case study encompasses logical agents and a deep learning (DL) component, to devise a system specialised in monitoring flood events for civil protection purposes. More precisely, we describe a prototypical framework consisting of a set of intelligent agents, which perform various tasks and communicate with each other to efficiently generate alerts during flood crisis events. Alerts are only delivered when at least two separates sources agree on an event on the same zone, i.e. aerial images and severe weather reports. Images are segmented by a neural network trained over eight classes of topographical entities. The resulting mask is analysed by a Logic Image Descriptor (LID) which then submit the perception to a logical agent.
{"title":"Neural-logic multi-agent system for flood event detection","authors":"Andrea Rafanelli, S. Costantini, Giovanni De Gasperis","doi":"10.3233/IA-230004","DOIUrl":"https://doi.org/10.3233/IA-230004","url":null,"abstract":" This paper shows the capabilities offered by an integrated neural-logic multi-agent system (MAS). Our case study encompasses logical agents and a deep learning (DL) component, to devise a system specialised in monitoring flood events for civil protection purposes. More precisely, we describe a prototypical framework consisting of a set of intelligent agents, which perform various tasks and communicate with each other to efficiently generate alerts during flood crisis events. Alerts are only delivered when at least two separates sources agree on an event on the same zone, i.e. aerial images and severe weather reports. Images are segmented by a neural network trained over eight classes of topographical entities. The resulting mask is analysed by a Logic Image Descriptor (LID) which then submit the perception to a logical agent.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43719707","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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