Ángel Alexander Cabrera, Marco Tulio Ribeiro, Bongshin Lee, R. Deline, Adam Perer, S. Drucker
Data scientists require rich mental models of how AI systems behave to effectively train, debug, and work with them. Despite the prevalence of AI analysis tools, there is no general theory describing how people make sense of what their models have learned. We frame this process as a form of sensemaking and derive a framework describing how data scientists develop mental models of AI behavior. To evaluate the framework, we show how existing AI analysis tools fit into this sensemaking process and use it to design AIFinnity, a system for analyzing image-and-text models. Lastly, we explored how data scientists use a tool developed with the framework through a think-aloud study with 10 data scientists tasked with using AIFinnity to pick an image captioning model. We found that AIFinnity’s sensemaking workflow reflected participants’ mental processes and enabled them to discover and validate diverse AI behaviors.
{"title":"What Did My AI Learn? How Data Scientists Make Sense of Model Behavior","authors":"Ángel Alexander Cabrera, Marco Tulio Ribeiro, Bongshin Lee, R. Deline, Adam Perer, S. Drucker","doi":"10.1145/3542921","DOIUrl":"https://doi.org/10.1145/3542921","url":null,"abstract":"Data scientists require rich mental models of how AI systems behave to effectively train, debug, and work with them. Despite the prevalence of AI analysis tools, there is no general theory describing how people make sense of what their models have learned. We frame this process as a form of sensemaking and derive a framework describing how data scientists develop mental models of AI behavior. To evaluate the framework, we show how existing AI analysis tools fit into this sensemaking process and use it to design AIFinnity, a system for analyzing image-and-text models. Lastly, we explored how data scientists use a tool developed with the framework through a think-aloud study with 10 data scientists tasked with using AIFinnity to pick an image captioning model. We found that AIFinnity’s sensemaking workflow reflected participants’ mental processes and enabled them to discover and validate diverse AI behaviors.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":"30 1","pages":"1 - 27"},"PeriodicalIF":3.7,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43958220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Innovations in machine learning are enabling organisational knowledge bases to be automatically generated from working people's activities. The potential for these to shift the ways in which knowledge is produced and shared raises questions about what types of knowledge might be inferred from working people's actions, how these can be used to support work, and what the broader ramifications of this might be. This paper draws on findings from studies of (i) collaborative actions, and (ii) knowledge actions, to explore how these actions might (i) inform automatically generated knowledge bases, and (ii) be better supported through technological innovation. We triangulate findings to develop a framework of actions that are performed as part of everyday work, and use this to explore how mining those actions could result in knowledge being explicitly and implicitly contributed to a knowledge base. We draw on these possibilities to highlight implications and considerations for responsible design.
{"title":"Building Knowledge through Action: Considerations for Machine Learning in the Workplace","authors":"Siân E. Lindley, Denise J. Wilkins","doi":"10.1145/3584947","DOIUrl":"https://doi.org/10.1145/3584947","url":null,"abstract":"Innovations in machine learning are enabling organisational knowledge bases to be automatically generated from working people's activities. The potential for these to shift the ways in which knowledge is produced and shared raises questions about what types of knowledge might be inferred from working people's actions, how these can be used to support work, and what the broader ramifications of this might be. This paper draws on findings from studies of (i) collaborative actions, and (ii) knowledge actions, to explore how these actions might (i) inform automatically generated knowledge bases, and (ii) be better supported through technological innovation. We triangulate findings to develop a framework of actions that are performed as part of everyday work, and use this to explore how mining those actions could result in knowledge being explicitly and implicitly contributed to a knowledge base. We draw on these possibilities to highlight implications and considerations for responsible design.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48468857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As computing technology comes to dominate every aspect of social and political life, HCI must take greater account of History. The article considers four different historical periods impacted by division and denunciation: the European Witch Hunts, the Soviet Purges, the McCarthy Era, and the Chinese Cultural Revolution. Historians have identified patterns common to such periods including: the unity of accusation and action; condemnation as a show of virtue, and defense of the accused as collusion with enemies. These patterns are mapped to findings from social media research such as: impulsive shares are easy to make but difficult to retract; angry posts travel fastest and furthest; likes and retweets express group identity and solidarity. Anachronistic memes, tweets and selfies explore what previous eras might have looked like if contemporary technology had existed in the past. It is argued that such anachronistic fiction may be a useful method for exploring the potential impact of particular design choices.
{"title":"Historically Informed HCI: Reflecting on Contemporary Technology through Anachronistic Fiction","authors":"Kien Mensonge","doi":"10.1145/3517144","DOIUrl":"https://doi.org/10.1145/3517144","url":null,"abstract":"As computing technology comes to dominate every aspect of social and political life, HCI must take greater account of History. The article considers four different historical periods impacted by division and denunciation: the European Witch Hunts, the Soviet Purges, the McCarthy Era, and the Chinese Cultural Revolution. Historians have identified patterns common to such periods including: the unity of accusation and action; condemnation as a show of virtue, and defense of the accused as collusion with enemies. These patterns are mapped to findings from social media research such as: impulsive shares are easy to make but difficult to retract; angry posts travel fastest and furthest; likes and retweets express group identity and solidarity. Anachronistic memes, tweets and selfies explore what previous eras might have looked like if contemporary technology had existed in the past. It is argued that such anachronistic fiction may be a useful method for exploring the potential impact of particular design choices.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":"29 1","pages":"1 - 39"},"PeriodicalIF":3.7,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46106288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article engages with history as a speculative space for the purpose of critically engaging with discourses around the politics of technology in HCI. Drawing on approaches within critical design and based on evidence from two different projects, we develop an approach, counterfactual actions, that moves beyond the creation of artifacts and towards more situated, embodied, and performative engagements. In one project, Reimaging Work, we used a participatory game to engage stakeholders from social and economic justice organizations in Chicago. The other project, Future Design Studio, invited audience members at a futurist festival to create artifacts from the future and then invited improvisational actors to build worlds around them. We argue that a focus on counterfactual actions supports a more relational approach to understanding the politics of socio-technical systems and infrastructures, allowing participants to gain a meaningful understanding of the ways in which technology could be designed otherwise in line with ethics, values and social justice concerns.
{"title":"Speculative Histories, Just Futures: From Counterfactual Artifacts to Counterfactual Actions","authors":"Laura E. Forlano, Megan K. Halpern","doi":"10.1145/3577212","DOIUrl":"https://doi.org/10.1145/3577212","url":null,"abstract":"This article engages with history as a speculative space for the purpose of critically engaging with discourses around the politics of technology in HCI. Drawing on approaches within critical design and based on evidence from two different projects, we develop an approach, counterfactual actions, that moves beyond the creation of artifacts and towards more situated, embodied, and performative engagements. In one project, Reimaging Work, we used a participatory game to engage stakeholders from social and economic justice organizations in Chicago. The other project, Future Design Studio, invited audience members at a futurist festival to create artifacts from the future and then invited improvisational actors to build worlds around them. We argue that a focus on counterfactual actions supports a more relational approach to understanding the politics of socio-technical systems and infrastructures, allowing participants to gain a meaningful understanding of the ways in which technology could be designed otherwise in line with ethics, values and social justice concerns.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":"30 1","pages":"1 - 37"},"PeriodicalIF":3.7,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47761204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. D. Zając, Dana Li, Xiang Dai, J. Carlsen, F. Kensing, T. Andersen
Artificial Intelligence (AI) in medical applications holds great promise. However, the use of Machine Learning-based (ML) systems in clinical practice is still minimal. It is uniquely difficult to introduce clinician-facing ML-based systems in practice, which has been recognised in HCI and related fields. Recent publications have begun to address the sociotechnical challenges of designing, developing, and successfully deploying clinician-facing ML-based systems. We conducted a qualitative systematic review and provided answers to the question: “How can HCI researchers and practitioners contribute to the successful realisation of ML in medical practice?” We reviewed 25 eligible papers that investigated the real-world clinical implications of concrete clinician-facing ML-based systems. The main contributions of this systematic review are: (1) an overview of the technical aspects of ML innovation and their consequences for HCI researchers and practitioners; (2) a description of the different roles that ML-based systems can take in clinical settings; (3) a conceptualisation of the main activities of medical ML innovation processes; (4) identification of five sociotechnical interdependencies that emerge from medical ML innovation; and (5) implications for HCI researchers and practitioners on how to mitigate the sociotechnical challenges of medical ML innovation.
{"title":"Clinician-Facing AI in the Wild: Taking Stock of the Sociotechnical Challenges and Opportunities for HCI","authors":"H. D. Zając, Dana Li, Xiang Dai, J. Carlsen, F. Kensing, T. Andersen","doi":"10.1145/3582430","DOIUrl":"https://doi.org/10.1145/3582430","url":null,"abstract":"Artificial Intelligence (AI) in medical applications holds great promise. However, the use of Machine Learning-based (ML) systems in clinical practice is still minimal. It is uniquely difficult to introduce clinician-facing ML-based systems in practice, which has been recognised in HCI and related fields. Recent publications have begun to address the sociotechnical challenges of designing, developing, and successfully deploying clinician-facing ML-based systems. We conducted a qualitative systematic review and provided answers to the question: “How can HCI researchers and practitioners contribute to the successful realisation of ML in medical practice?” We reviewed 25 eligible papers that investigated the real-world clinical implications of concrete clinician-facing ML-based systems. The main contributions of this systematic review are: (1) an overview of the technical aspects of ML innovation and their consequences for HCI researchers and practitioners; (2) a description of the different roles that ML-based systems can take in clinical settings; (3) a conceptualisation of the main activities of medical ML innovation processes; (4) identification of five sociotechnical interdependencies that emerge from medical ML innovation; and (5) implications for HCI researchers and practitioners on how to mitigate the sociotechnical challenges of medical ML innovation.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":"30 1","pages":"1 - 39"},"PeriodicalIF":3.7,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43253058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When computers unexpectedly delay or thwart goal attainment, frustration ensues. The central studies of the extent, content, and impact of such frustration were done more than 15 years ago. We revisit this issue after computers have become more mature and computer use is more extensive. To this end, we had 234 crowdsourced participants log the frustrating episodes they experienced with their computers during one hour of computer use. The average time lost due to frustrating episodes was between 11% and 20% of the one-hour period. Though this is less time lost than in the earlier studies, frustration remains a common user experience. While shorter, the median level of frustration during the episodes was high (7 on a 9-point scale). The frustration level correlated with task importance and time lost but was unaffected by computer experience and largely unaffected by computer self-efficacy. In addition, participants indicated that 84% of the episodes had happened before, that 87% could happen again, and that they were unable to resolve 26% of the episodes. This high rate of recurrence and lack of control likely added to the frustration level. The episodes spanned various issues pertaining to performance (49%), usability (36%), and utility (16%).
{"title":"Frustration: Still a Common User Experience","authors":"M. Hertzum, K. Hornbæk","doi":"10.1145/3582432","DOIUrl":"https://doi.org/10.1145/3582432","url":null,"abstract":"When computers unexpectedly delay or thwart goal attainment, frustration ensues. The central studies of the extent, content, and impact of such frustration were done more than 15 years ago. We revisit this issue after computers have become more mature and computer use is more extensive. To this end, we had 234 crowdsourced participants log the frustrating episodes they experienced with their computers during one hour of computer use. The average time lost due to frustrating episodes was between 11% and 20% of the one-hour period. Though this is less time lost than in the earlier studies, frustration remains a common user experience. While shorter, the median level of frustration during the episodes was high (7 on a 9-point scale). The frustration level correlated with task importance and time lost but was unaffected by computer experience and largely unaffected by computer self-efficacy. In addition, participants indicated that 84% of the episodes had happened before, that 87% could happen again, and that they were unable to resolve 26% of the episodes. This high rate of recurrence and lack of control likely added to the frustration level. The episodes spanned various issues pertaining to performance (49%), usability (36%), and utility (16%).","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":"30 1","pages":"1 - 26"},"PeriodicalIF":3.7,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41385459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Spence, B. Koleva, Steve Benford, Dimitrios Paris Darzentas, Martin Flintham, Kevin Glover, H. Wagner, Rebecca Gibson, Emily-Clare Thorn
Gifting is socially and economically important. Studies of gifting physical objects have revealed motivations, values, and the tensions between them, while HCI research has revealed weaknesses of digital gifting and explored possibilities of hybrid gifting. We report an “in the wild” study of a hybrid chocolate gift deployed as a commercial product. Interviews reveal the experiences of receivers and givers, as well as the producer's friction points and tangible benefits. We reveal how in hybrid gifts the digital elevates the physical while the physical grounds the digital. We discuss how hybrid gifts bridge the tension between receiver-preference and relationship-signalling motivations, the need to further strengthen the exchange and reveal stages of hybrid gifting, and to manage the privacy of sensitive personal messages. We propose to extend the concept of hybrid wrapping to include a finer-grained interleaving of digital into complex packaging and multi-layered wrappings to create more holistic gifting experiences.
{"title":"“More than a cliché”: Experiencing Hybrid Gifting in the Wild","authors":"J. Spence, B. Koleva, Steve Benford, Dimitrios Paris Darzentas, Martin Flintham, Kevin Glover, H. Wagner, Rebecca Gibson, Emily-Clare Thorn","doi":"10.1145/3577015","DOIUrl":"https://doi.org/10.1145/3577015","url":null,"abstract":"Gifting is socially and economically important. Studies of gifting physical objects have revealed motivations, values, and the tensions between them, while HCI research has revealed weaknesses of digital gifting and explored possibilities of hybrid gifting. We report an “in the wild” study of a hybrid chocolate gift deployed as a commercial product. Interviews reveal the experiences of receivers and givers, as well as the producer's friction points and tangible benefits. We reveal how in hybrid gifts the digital elevates the physical while the physical grounds the digital. We discuss how hybrid gifts bridge the tension between receiver-preference and relationship-signalling motivations, the need to further strengthen the exchange and reveal stages of hybrid gifting, and to manage the privacy of sensitive personal messages. We propose to extend the concept of hybrid wrapping to include a finer-grained interleaving of digital into complex packaging and multi-layered wrappings to create more holistic gifting experiences.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":"30 1","pages":"1 - 31"},"PeriodicalIF":3.7,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46913113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. J. Amon, Aaron Necaise, N. Kartvelishvili, Aneka Williams, Yan Solihin, Apu Kapadia
“Interdependent” privacy violations occur when users share private photos and information about other people in social media without permission. This research investigated user characteristics associated with interdependent privacy perceptions, by asking social media users to rate photo-based memes depicting strangers on the degree to which they were too private to share. Users also completed questionnaires measuring social media usage and personality. Separate groups rated the memes on shareability, valence, and entertainment value. Users were less likely to share memes that were rated as private, except when the meme was entertaining or when users exhibited dark triad characteristics. Users with dark triad characteristics demonstrated a heightened awareness of interdependent privacy and increased sharing of others’ photos. A model is introduced that highlights user types and characteristics that correspond to different privacy preferences: privacy preservers, ignorers, and violators. We discuss how interventions to support interdependent privacy must effectively influence diverse users.
{"title":"Modeling User Characteristics Associated with Interdependent Privacy Perceptions on Social Media","authors":"M. J. Amon, Aaron Necaise, N. Kartvelishvili, Aneka Williams, Yan Solihin, Apu Kapadia","doi":"10.1145/3577014","DOIUrl":"https://doi.org/10.1145/3577014","url":null,"abstract":"“Interdependent” privacy violations occur when users share private photos and information about other people in social media without permission. This research investigated user characteristics associated with interdependent privacy perceptions, by asking social media users to rate photo-based memes depicting strangers on the degree to which they were too private to share. Users also completed questionnaires measuring social media usage and personality. Separate groups rated the memes on shareability, valence, and entertainment value. Users were less likely to share memes that were rated as private, except when the meme was entertaining or when users exhibited dark triad characteristics. Users with dark triad characteristics demonstrated a heightened awareness of interdependent privacy and increased sharing of others’ photos. A model is introduced that highlights user types and characteristics that correspond to different privacy preferences: privacy preservers, ignorers, and violators. We discuss how interventions to support interdependent privacy must effectively influence diverse users.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":"30 1","pages":"1 - 32"},"PeriodicalIF":3.7,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42219653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laia Turmo Vidal, Elena Márquez Segura, Annika Wærn
{"title":"Intercorporeal Biofeedback for Movement Learning","authors":"Laia Turmo Vidal, Elena Márquez Segura, Annika Wærn","doi":"10.1145/3582428","DOIUrl":"https://doi.org/10.1145/3582428","url":null,"abstract":"","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":"30 1","pages":"43:1-43:40"},"PeriodicalIF":3.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64066013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the limitations imposed by the COVID-19 pandemic, customers have shifted their shopping patterns from offline to online. Livestream shopping has become popular as one of the online shopping media. However, various streamers’ malicious selling behaviors have been reported. In this research, we sought to explore streamers’ malicious selling strategies and understand how viewers perceive these strategies. First, we recorded 40 livestream shopping sessions from two popular livestream platforms in China—Taobao, and TikTok. We identified 16 malicious selling strategies that were used to deceive, coerce, or manipulate viewers and found that platform designs enhanced nine of the malicious selling strategies. Second, through an interview study with 13 viewers, we report three challenges of overcoming malicious selling in relation to imbalanced power between viewers, streamers, and the platforms. We conclude by discussing the policy and design implications of countering malicious selling.
{"title":"Malicious Selling Strategies in Livestream E-commerce: A Case Study of Alibaba’s Taobao and ByteDance’s TikTok","authors":"Qunfang Wu, Yisi Sang, Dakuo Wang, Zhicong Lu","doi":"10.1145/3577199","DOIUrl":"https://doi.org/10.1145/3577199","url":null,"abstract":"Due to the limitations imposed by the COVID-19 pandemic, customers have shifted their shopping patterns from offline to online. Livestream shopping has become popular as one of the online shopping media. However, various streamers’ malicious selling behaviors have been reported. In this research, we sought to explore streamers’ malicious selling strategies and understand how viewers perceive these strategies. First, we recorded 40 livestream shopping sessions from two popular livestream platforms in China—Taobao, and TikTok. We identified 16 malicious selling strategies that were used to deceive, coerce, or manipulate viewers and found that platform designs enhanced nine of the malicious selling strategies. Second, through an interview study with 13 viewers, we report three challenges of overcoming malicious selling in relation to imbalanced power between viewers, streamers, and the platforms. We conclude by discussing the policy and design implications of countering malicious selling.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":" ","pages":"1 - 29"},"PeriodicalIF":3.7,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49006994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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