Pub Date : 2025-03-06DOI: 10.1109/jproc.2025.3542324
Minqiang Yang, Edith C. H. Ngai, Xiping Hu, Bin Hu, Jiangchuan Liu, Erol Gelenbe, Victor C. M. Leung
{"title":"Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection","authors":"Minqiang Yang, Edith C. H. Ngai, Xiping Hu, Bin Hu, Jiangchuan Liu, Erol Gelenbe, Victor C. M. Leung","doi":"10.1109/jproc.2025.3542324","DOIUrl":"https://doi.org/10.1109/jproc.2025.3542324","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"38 1","pages":""},"PeriodicalIF":20.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569759","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 : 2025-02-04DOI: 10.1109/JPROC.2025.3528152
{"title":"Proceedings of the IEEE: Stay Informed. Become Inspired.","authors":"","doi":"10.1109/JPROC.2025.3528152","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3528152","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 11","pages":"C4-C4"},"PeriodicalIF":23.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105645","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 : 2025-02-04DOI: 10.1109/JPROC.2025.3530306
{"title":"IEEE Connects You to a Universe of Information","authors":"","doi":"10.1109/JPROC.2025.3530306","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3530306","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 11","pages":"1756-1756"},"PeriodicalIF":23.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870482","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105644","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 : 2025-02-04DOI: 10.1109/JPROC.2025.3528148
{"title":"Future Special Issues/Special Sections of the Proceedings","authors":"","doi":"10.1109/JPROC.2025.3528148","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3528148","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 11","pages":"1755-1755"},"PeriodicalIF":23.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107185","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 : 2025-02-04DOI: 10.1109/JPROC.2025.3528558
Summary form only: Abstracts of articles presented in this issue of the publication.
{"title":"Scanning the Issue","authors":"","doi":"10.1109/JPROC.2025.3528558","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3528558","url":null,"abstract":"Summary form only: Abstracts of articles presented in this issue of the publication.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 11","pages":"1646-1648"},"PeriodicalIF":23.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870483","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107118","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 : 2025-02-04DOI: 10.1109/JPROC.2025.3528144
{"title":"Proceedings of the IEEE Publication Information","authors":"","doi":"10.1109/JPROC.2025.3528144","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3528144","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 11","pages":"C2-C2"},"PeriodicalIF":23.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106890","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 : 2025-02-04DOI: 10.1109/JPROC.2025.3528150
{"title":"IEEE Membership","authors":"","doi":"10.1109/JPROC.2025.3528150","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3528150","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 11","pages":"C3-C3"},"PeriodicalIF":23.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870451","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107186","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 : 2025-01-27DOI: 10.1109/JPROC.2024.3520707
Tilahun M. Getu;Georges Kaddoum;Mehdi Bennis
Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national laboratories have been developing proposals for the sixth-generation (6G), whose materialization is fraught with many fundamental challenges. To alleviate these challenges, a deep learning (DL)-enabled semantic communication (SemCom) has emerged as a promising 6G technology enabler, which embodies a paradigm shift that can change the status quo viewpoint that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meanings have been ignored. Since 6G is also critical for the materialization of major SemCom use cases, the paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration of 6G and SemCom. For this purpose, this comprehensive article provides the fundamentals of semantic information, semantic representation, and semantic entropy; details the state-of-the-art SemCom research landscape; presents the major SemCom trends and use cases; discusses current SemCom theories; exposes the fundamental and major challenges of SemCom; and offers future research directions for SemCom. We hope this article stimulates many lines of research on SemCom theories, algorithms, and implementation.
{"title":"Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions","authors":"Tilahun M. Getu;Georges Kaddoum;Mehdi Bennis","doi":"10.1109/JPROC.2024.3520707","DOIUrl":"10.1109/JPROC.2024.3520707","url":null,"abstract":"Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national laboratories have been developing proposals for the sixth-generation (6G), whose materialization is fraught with many fundamental challenges. To alleviate these challenges, a deep learning (DL)-enabled semantic communication (SemCom) has emerged as a promising 6G technology enabler, which embodies a paradigm shift that can change the status quo viewpoint that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meanings have been ignored. Since 6G is also critical for the materialization of major SemCom use cases, the paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration of 6G and SemCom. For this purpose, this comprehensive article provides the fundamentals of semantic information, semantic representation, and semantic entropy; details the state-of-the-art SemCom research landscape; presents the major SemCom trends and use cases; discusses current SemCom theories; exposes the fundamental and major challenges of SemCom; and offers future research directions for SemCom. We hope this article stimulates many lines of research on SemCom theories, algorithms, and implementation.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 11","pages":"1649-1685"},"PeriodicalIF":23.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050366","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 : 2025-01-23DOI: 10.1109/JPROC.2024.3520803
Xiaoxu Ren;Minrui Xu;Dusit Niyato;Jiawen Kang;Zehui Xiong;Chao Qiu;Haipeng Yao;Xiaofei Wang
Web 3.0 pursues the establishment of decentralized ecosystems through blockchain technologies, driving digital transformation in commerce and governance. With consensus algorithms and smart contracts grounded in cryptographic technologies, Web 3.0 enables secure and transparent digital services, such as digital identity, asset management, decentralized autonomous organizations (DAOs), and decentralized finance (DeFi), fostering integration between digital and physical economies. As quantum devices rapidly advance, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with enhanced quantum computing and communication capabilities. This article provides a comprehensive overview of blockchain-based Web 3.0, examining its quantum and postquantum advancements from two key perspectives. On the one hand, postquantum migration methods and quantum-resistant signatures offer robust solutions to safeguard blockchain against quantum threats. On the other hand, quantum and postquantum encryption and verification algorithms boost blockchain performance, creating a decentralized, secure, and value-driven system. Additionally, we outline potential applications of quantum blockchain and offer guidance for implementation within the Web 3.0 ecosystem. Finally, we discuss future directions for developing a provably secure and decentralized digital ecosystem.
{"title":"Building Resilient Web 3.0 Infrastructure With Quantum Information Technologies and Blockchain: An Ambilateral View","authors":"Xiaoxu Ren;Minrui Xu;Dusit Niyato;Jiawen Kang;Zehui Xiong;Chao Qiu;Haipeng Yao;Xiaofei Wang","doi":"10.1109/JPROC.2024.3520803","DOIUrl":"10.1109/JPROC.2024.3520803","url":null,"abstract":"Web 3.0 pursues the establishment of decentralized ecosystems through blockchain technologies, driving digital transformation in commerce and governance. With consensus algorithms and smart contracts grounded in cryptographic technologies, Web 3.0 enables secure and transparent digital services, such as digital identity, asset management, decentralized autonomous organizations (DAOs), and decentralized finance (DeFi), fostering integration between digital and physical economies. As quantum devices rapidly advance, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with enhanced quantum computing and communication capabilities. This article provides a comprehensive overview of blockchain-based Web 3.0, examining its quantum and postquantum advancements from two key perspectives. On the one hand, postquantum migration methods and quantum-resistant signatures offer robust solutions to safeguard blockchain against quantum threats. On the other hand, quantum and postquantum encryption and verification algorithms boost blockchain performance, creating a decentralized, secure, and value-driven system. Additionally, we outline potential applications of quantum blockchain and offer guidance for implementation within the Web 3.0 ecosystem. Finally, we discuss future directions for developing a provably secure and decentralized digital ecosystem.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 11","pages":"1686-1715"},"PeriodicalIF":23.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026556","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 : 2025-01-15DOI: 10.1109/JPROC.2024.3525147
Tianshi Wang;Fengling Li;Lei Zhu;Jingjing Li;Zheng Zhang;Heng Tao Shen
With the exponential surge in diverse multimodal data, traditional unimodal retrieval methods struggle to meet the needs of users seeking access to data across various modalities. To address this, cross-modal retrieval has emerged, enabling interaction across modalities, facilitating semantic matching, and leveraging complementarity and consistency between heterogeneous data. Although prior literature has reviewed the field of cross-modal retrieval, it suffers from numerous deficiencies in terms of timeliness, taxonomy, and comprehensiveness. This article conducts a comprehensive review of cross-modal retrieval’s evolution, spanning from shallow statistical analysis techniques to vision-language pretraining (VLP) models. Commencing with a comprehensive taxonomy grounded in machine learning paradigms, mechanisms, and models, this article delves deeply into the principles and architectures underpinning existing cross-modal retrieval methods. Furthermore, it offers an overview of widely used benchmarks, metrics, and performances. Lastly, this article probes the prospects and challenges that confront contemporary cross-modal retrieval, while engaging in a discourse on potential directions for further progress in the field. To facilitate the ongoing research on cross-modal retrieval, we develop a user-friendly toolbox and an open-source repository at https://cross-modal-retrieval.github.io.
{"title":"Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions","authors":"Tianshi Wang;Fengling Li;Lei Zhu;Jingjing Li;Zheng Zhang;Heng Tao Shen","doi":"10.1109/JPROC.2024.3525147","DOIUrl":"10.1109/JPROC.2024.3525147","url":null,"abstract":"With the exponential surge in diverse multimodal data, traditional unimodal retrieval methods struggle to meet the needs of users seeking access to data across various modalities. To address this, cross-modal retrieval has emerged, enabling interaction across modalities, facilitating semantic matching, and leveraging complementarity and consistency between heterogeneous data. Although prior literature has reviewed the field of cross-modal retrieval, it suffers from numerous deficiencies in terms of timeliness, taxonomy, and comprehensiveness. This article conducts a comprehensive review of cross-modal retrieval’s evolution, spanning from shallow statistical analysis techniques to vision-language pretraining (VLP) models. Commencing with a comprehensive taxonomy grounded in machine learning paradigms, mechanisms, and models, this article delves deeply into the principles and architectures underpinning existing cross-modal retrieval methods. Furthermore, it offers an overview of widely used benchmarks, metrics, and performances. Lastly, this article probes the prospects and challenges that confront contemporary cross-modal retrieval, while engaging in a discourse on potential directions for further progress in the field. To facilitate the ongoing research on cross-modal retrieval, we develop a user-friendly toolbox and an open-source repository at <uri>https://cross-modal-retrieval.github.io</uri>.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 11","pages":"1716-1754"},"PeriodicalIF":23.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986514","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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