Pub Date : 2023-10-12DOI: 10.1109/JPROC.2023.3316236
{"title":"Future Special Issues/Special Sections of the Proceedings","authors":"","doi":"10.1109/JPROC.2023.3316236","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3316236","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 10","pages":"1459-1459"},"PeriodicalIF":20.6,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5/10283866/10284000.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67915804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1109/JPROC.2023.3318028
Björn W. Schuller;Matti Pietikäinen
The articles in this special issue cover four major subfields in affective computing, namely affect analysis, affect synthesis, applications, and ethics.
本期特刊中的文章涵盖了情感计算的四个主要子领域，即情感分析、情感综合、应用和伦理学。
{"title":"Affective Computing [Scanning the Issue]","authors":"Björn W. Schuller;Matti Pietikäinen","doi":"10.1109/JPROC.2023.3318028","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3318028","url":null,"abstract":"The articles in this special issue cover four major subfields in affective computing, namely affect analysis, affect synthesis, applications, and ethics.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 10","pages":"1139-1141"},"PeriodicalIF":20.6,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5/10283866/10283958.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67759659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1109/JPROC.2023.3316232
{"title":"Proceedings of the IEEE Publication Information","authors":"","doi":"10.1109/JPROC.2023.3316232","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3316232","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 10","pages":"C2-C2"},"PeriodicalIF":20.6,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5/10283866/10283908.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67759657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1109/JPROC.2023.3316238
{"title":"IEEE Membership","authors":"","doi":"10.1109/JPROC.2023.3316238","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3316238","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 10","pages":"C3-C3"},"PeriodicalIF":20.6,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5/10283866/10283887.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67915805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1109/JPROC.2023.3319911
{"title":"IEEE Women in Engineering","authors":"","doi":"10.1109/JPROC.2023.3319911","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3319911","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 10","pages":"1460-1460"},"PeriodicalIF":20.6,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5/10283866/10283909.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67760313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1109/JPROC.2023.3316240
{"title":"Proceedings of the IEEE: Stay Informed. Become Inspired.","authors":"","doi":"10.1109/JPROC.2023.3316240","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3316240","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 10","pages":"C4-C4"},"PeriodicalIF":20.6,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5/10283866/10283890.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67760149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-03DOI: 10.1109/JPROC.2023.3315689
Georgios N. Yannakakis;David Melhart
This article surveys the current state-of-the-art in affective computing (AC) principles, methods, and tools as applied to games. We review this emerging field, namely affective game computing, through the lens of the four core phases of the affective loop: game affect elicitation, game affect sensing, game affect detection, and game affect adaptation. In addition, we provide a taxonomy of terms, methods, and approaches used across the four phases of the affective game loop and situate the field within this taxonomy. We continue with a comprehensive review of available affect data collection methods with regard to gaming interfaces, sensors, annotation protocols, and available corpora. This article concludes with a discussion on the current limitations of affective game computing and our vision for the most promising future research directions in the field.
{"title":"Affective Game Computing: A Survey","authors":"Georgios N. Yannakakis;David Melhart","doi":"10.1109/JPROC.2023.3315689","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3315689","url":null,"abstract":"This article surveys the current state-of-the-art in affective computing (AC) principles, methods, and tools as applied to games. We review this emerging field, namely affective game computing, through the lens of the four core phases of the affective loop: game affect elicitation, game affect sensing, game affect detection, and game affect adaptation. In addition, we provide a taxonomy of terms, methods, and approaches used across the four phases of the affective game loop and situate the field within this taxonomy. We continue with a comprehensive review of available affect data collection methods with regard to gaming interfaces, sensors, annotation protocols, and available corpora. This article concludes with a discussion on the current limitations of affective game computing and our vision for the most promising future research directions in the field.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 10","pages":"1423-1444"},"PeriodicalIF":20.6,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67760315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1109/JPROC.2023.3315217
Laurence Devillers;Roddy Cowie
Affective computing develops systems, which recognize or influence aspects of human life related to emotion, including feelings and attitudes. Significant potential for both good and harm makes it ethically sensitive, and trying to strike sound balances is challenging. Common images of the issues invite oversimplification and offer a limited understanding of the moral consequences and ethical tensions. Considering the state-of-the-art shows how pervasive and complex they are. In many areas, the discipline can potentially bring ethically significant benefits and hence has a duty to try. They include making interactions with machines more effective and less stressful, diagnostic and therapeutic roles in emotion-related disorders, intelligent tutoring, and reducing isolation. However, the limits of recognition technology mean that actions are likely to be based on impoverished representations of people’s affective state, particularly with certain groups; systems are liable to arouse feelings that are positive, but not well grounded in reality, affectively engaging systems can become addictive and manipulative, and they confer dangerous power on those who control the technology. We offer an overview of those and other particular ethical issues, positive and negative, which arise from the current state of affective computing. It aims to reflect the complexities inherent in both the technology and current ethical discussions. Establishing appropriate responses is a challenge for society as a whole, not only the affective computing community.
{"title":"Ethical Considerations on Affective Computing: An Overview","authors":"Laurence Devillers;Roddy Cowie","doi":"10.1109/JPROC.2023.3315217","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3315217","url":null,"abstract":"Affective computing develops systems, which recognize or influence aspects of human life related to emotion, including feelings and attitudes. Significant potential for both good and harm makes it ethically sensitive, and trying to strike sound balances is challenging. Common images of the issues invite oversimplification and offer a limited understanding of the moral consequences and ethical tensions. Considering the state-of-the-art shows how pervasive and complex they are. In many areas, the discipline can potentially bring ethically significant benefits and hence has a duty to try. They include making interactions with machines more effective and less stressful, diagnostic and therapeutic roles in emotion-related disorders, intelligent tutoring, and reducing isolation. However, the limits of recognition technology mean that actions are likely to be based on impoverished representations of people’s affective state, particularly with certain groups; systems are liable to arouse feelings that are positive, but not well grounded in reality, affectively engaging systems can become addictive and manipulative, and they confer dangerous power on those who control the technology. We offer an overview of those and other particular ethical issues, positive and negative, which arise from the current state of affective computing. It aims to reflect the complexities inherent in both the technology and current ethical discussions. Establishing appropriate responses is a challenge for society as a whole, not only the affective computing community.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 10","pages":"1445-1458"},"PeriodicalIF":20.6,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67760314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emotion and sentiment play a central role in various human activities, such as perception, decision-making, social interaction, and logical reasoning. Developing artificial emotional intelligence (AEI) for machines is becoming a bottleneck in human–computer interaction. The first step of AEI is to recognize the emotion and sentiment that are conveyed in different affective signals. Traditional supervised emotion and sentiment analysis (ESA) methods, especially deep learning-based ones, usually require large-scale labeled training data. However, due to the essential subjectivity, complexity, uncertainty and ambiguity, and subtlety, collecting such annotations is expensive, time-consuming, and difficult in practice. In this article, we introduce label-efficient ESA from the computational perspective. First, we present a hierarchical taxonomy for label-efficient learning based on the availability of sample labels, emotion categories, and data domains during training. Second, for each of the seven paradigms, i.e., unsupervised, semisupervised, weakly supervised, low-shot, incremental, domain-adaptive, and domain-generalizable ESA, we give the definition, summarize existing methods, and present our views on the quantitative and qualitative comparison. Finally, we provide several promising real-world applications, followed by unsolved challenges and potential future directions.
{"title":"Toward Label-Efficient Emotion and Sentiment Analysis","authors":"Sicheng Zhao;Xiaopeng Hong;Jufeng Yang;Yanyan Zhao;Guiguang Ding","doi":"10.1109/JPROC.2023.3309299","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3309299","url":null,"abstract":"Emotion and sentiment play a central role in various human activities, such as perception, decision-making, social interaction, and logical reasoning. Developing artificial emotional intelligence (AEI) for machines is becoming a bottleneck in human–computer interaction. The first step of AEI is to recognize the emotion and sentiment that are conveyed in different affective signals. Traditional supervised emotion and sentiment analysis (ESA) methods, especially deep learning-based ones, usually require large-scale labeled training data. However, due to the essential subjectivity, complexity, uncertainty and ambiguity, and subtlety, collecting such annotations is expensive, time-consuming, and difficult in practice. In this article, we introduce label-efficient ESA from the computational perspective. First, we present a hierarchical taxonomy for label-efficient learning based on the availability of sample labels, emotion categories, and data domains during training. Second, for each of the seven paradigms, i.e., unsupervised, semisupervised, weakly supervised, low-shot, incremental, domain-adaptive, and domain-generalizable ESA, we give the definition, summarize existing methods, and present our views on the quantitative and qualitative comparison. Finally, we provide several promising real-world applications, followed by unsolved challenges and potential future directions.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 10","pages":"1159-1197"},"PeriodicalIF":20.6,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67759660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.1109/JPROC.2023.3306773
Chuan Ma;Jun Li;Kang Wei;Bo Liu;Ming Ding;Long Yuan;Zhu Han;H. Vincent Poor
Motivated by the advancing computational capacity of distributed end-user equipment (UE), as well as the increasing concerns about sharing private data, there has been considerable recent interest in machine learning (ML) and artificial intelligence (AI) that can be processed on distributed UEs. Specifically, in this paradigm, parts of an ML process are outsourced to multiple distributed UEs. Then, the processed information is aggregated on a certain level at a central server, which turns a centralized ML process into a distributed one and brings about significant benefits. However, this new distributed ML paradigm raises new risks in terms of privacy and security issues. In this article, we provide a survey of the emerging security and privacy risks of distributed ML from a unique perspective of information exchange levels, which are defined according to the key steps of an ML process, i.e., we consider the following levels: 1) the level of preprocessed data; 2) the level of learning models; 3) the level of extracted knowledge; and 4) the level of intermediate results. We explore and analyze the potential of threats for each information exchange level based on an overview of current state-of-the-art attack mechanisms and then discuss the possible defense methods against such threats. Finally, we complete the survey by providing an outlook on the challenges and possible directions for future research in this critical area.
{"title":"Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning","authors":"Chuan Ma;Jun Li;Kang Wei;Bo Liu;Ming Ding;Long Yuan;Zhu Han;H. Vincent Poor","doi":"10.1109/JPROC.2023.3306773","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3306773","url":null,"abstract":"Motivated by the advancing computational capacity of distributed end-user equipment (UE), as well as the increasing concerns about sharing private data, there has been considerable recent interest in machine learning (ML) and artificial intelligence (AI) that can be processed on distributed UEs. Specifically, in this paradigm, parts of an ML process are outsourced to multiple distributed UEs. Then, the processed information is aggregated on a certain level at a central server, which turns a centralized ML process into a distributed one and brings about significant benefits. However, this new distributed ML paradigm raises new risks in terms of privacy and security issues. In this article, we provide a survey of the emerging security and privacy risks of distributed ML from a unique perspective of information exchange levels, which are defined according to the key steps of an ML process, i.e., we consider the following levels: 1) the level of preprocessed data; 2) the level of learning models; 3) the level of extracted knowledge; and 4) the level of intermediate results. We explore and analyze the potential of threats for each information exchange level based on an overview of current state-of-the-art attack mechanisms and then discuss the possible defense methods against such threats. Finally, we complete the survey by providing an outlook on the challenges and possible directions for future research in this critical area.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 9","pages":"1097-1132"},"PeriodicalIF":20.6,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67837221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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