Proceedings of the IEEE最新文献

Future Special Issues/Special Sections of the Proceedings 未来的特别问题/诉讼程序的特别部分

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-12 DOI: 10.1109/JPROC.2023.3316236

引用次数: 0

Affective Computing [Scanning the Issue] 情感计算[扫描问题]

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-12 DOI: 10.1109/JPROC.2023.3318028

Björn W. Schuller;Matti Pietikäinen

引用次数: 0

Proceedings of the IEEE Publication Information IEEE出版信息汇编

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-12 DOI: 10.1109/JPROC.2023.3316232

引用次数: 0

IEEE Membership IEEE成员

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-12 DOI: 10.1109/JPROC.2023.3316238

引用次数: 0

IEEE Women in Engineering IEEE女性工程

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-12 DOI: 10.1109/JPROC.2023.3319911

引用次数: 0

Proceedings of the IEEE: Stay Informed. Become Inspired. IEEE会议记录：保持知情。获得灵感。

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-12 DOI: 10.1109/JPROC.2023.3316240

引用次数: 0

Affective Game Computing: A Survey 情感游戏计算研究综述

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-03 DOI: 10.1109/JPROC.2023.3315689

Georgios N. Yannakakis;David Melhart

引用次数: 0

Ethical Considerations on Affective Computing: An Overview 情感计算的伦理思考：综述

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-02 DOI: 10.1109/JPROC.2023.3315217

Laurence Devillers;Roddy Cowie

{"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}

引用次数: 0

Toward Label-Efficient Emotion and Sentiment Analysis 标签有效情感与情感分析

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-09-15 DOI: 10.1109/JPROC.2023.3309299

Sicheng Zhao;Xiaopeng Hong;Jufeng Yang;Yanyan Zhao;Guiguang Ding

{"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}

引用次数: 0

Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning 多代理系统中的可信人工智能：分布式学习的隐私和安全综述

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-09-14 DOI: 10.1109/JPROC.2023.3306773

Chuan Ma;Jun Li;Kang Wei;Bo Liu;Ming Ding;Long Yuan;Zhu Han;H. Vincent Poor

{"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}

引用次数: 8