Pub Date : 2024-08-29DOI: 10.1109/TCE.2024.3378723
Zhigao Zheng;Shahid Mumtaz;Joel J. P. C. Rodrigues;Bo Ai
Intelligent data processing harnesses the power of learning, analytics, and automated data insight extraction to guide managers through decision making, which emerged as imperative tools in computer science and information processing. However, the huge amount and complexity of the data acquired pose great challenges for processing and analysis. A graph is made up of nodes - real-life entities, like health care providers (HCPs), Integrated Delivery Networks (IDNs), and products - that can be connected to signify relationships. This model can represent real-world relationships much more clearly than relational databases that have rigid schemas. Leading consumer applications, like LinkedIn and Facebook, utilize graphs to easily identify and visualize complex relationships in a simple interface. This technology is an elegant, powerful way to solve complex data problems.
{"title":"Guest Editorial of the Special section on Graph-Powered Intelligent Data Processing for Consumer Electronics","authors":"Zhigao Zheng;Shahid Mumtaz;Joel J. P. C. Rodrigues;Bo Ai","doi":"10.1109/TCE.2024.3378723","DOIUrl":"https://doi.org/10.1109/TCE.2024.3378723","url":null,"abstract":"Intelligent data processing harnesses the power of learning, analytics, and automated data insight extraction to guide managers through decision making, which emerged as imperative tools in computer science and information processing. However, the huge amount and complexity of the data acquired pose great challenges for processing and analysis. A graph is made up of nodes - real-life entities, like health care providers (HCPs), Integrated Delivery Networks (IDNs), and products - that can be connected to signify relationships. This model can represent real-world relationships much more clearly than relational databases that have rigid schemas. Leading consumer applications, like LinkedIn and Facebook, utilize graphs to easily identify and visualize complex relationships in a simple interface. This technology is an elegant, powerful way to solve complex data problems.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 2","pages":"4894-4897"},"PeriodicalIF":4.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659261","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-29DOI: 10.1109/TCE.2024.3383389
Long Cheng;Qingzhi Liu;LeiYang Yang;John Murphy
The Consumer Internet of Things (or CIoT) refers to the vast number of physical personal devices, ranging from very simple ones such as fitness trackers to high-end smart electric vehicles, that are connected to the Internet. This connectivity has eliminated the barriers between the digital and physical worlds and allows us to use the data generated by these devices to improve various aspects of our lives.
{"title":"Guest Editorial of the Special Section on Intelligent Computing for Big Data in Consumer Internet of Things","authors":"Long Cheng;Qingzhi Liu;LeiYang Yang;John Murphy","doi":"10.1109/TCE.2024.3383389","DOIUrl":"https://doi.org/10.1109/TCE.2024.3383389","url":null,"abstract":"The Consumer Internet of Things (or CIoT) refers to the vast number of physical personal devices, ranging from very simple ones such as fitness trackers to high-end smart electric vehicles, that are connected to the Internet. This connectivity has eliminated the barriers between the digital and physical worlds and allows us to use the data generated by these devices to improve various aspects of our lives.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 2","pages":"4958-4960"},"PeriodicalIF":4.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-29DOI: 10.1109/TCE.2024.3380017
Mingbo Zhao;Sheng Li;Xiaojie Jin;Zhiwei Gao
Consumer electronics are electronic equipment intended for everyday use, and they constitute a part of the wider electronics industry including devices and services used for entertainment, communications and recreation. In practice, consumer electronics use digital technologies to enhance performance and well-being in realworld applications, such as traffic surveillance elements, online retailing, automatic driving systems, fashion and apparel industry, et al., where the information in these applications usually comes through multimedia data. For example, the videos in traffic surveillance contain both acoustic and visual signals; the online product in online retailing app or website include both image, video, text and even acoustic information; the sensory perceptions typically used in automatic driving system may need extensive multi-media data from multichannel inputs in visual, auditory and motor pathways. Thereby, how to characterize the property of multimedia data so that it can be managed to enable different learning tasks of various applications in consumer electronics, is of great importance. This requires researchers to develop robust models to classify, retrieve and understand multi-media information in these real-world applications.
{"title":"Guest Editorial of the Special Section on Multimedia Sensing and Computing for Consumer Electronics","authors":"Mingbo Zhao;Sheng Li;Xiaojie Jin;Zhiwei Gao","doi":"10.1109/TCE.2024.3380017","DOIUrl":"https://doi.org/10.1109/TCE.2024.3380017","url":null,"abstract":"Consumer electronics are electronic equipment intended for everyday use, and they constitute a part of the wider electronics industry including devices and services used for entertainment, communications and recreation. In practice, consumer electronics use digital technologies to enhance performance and well-being in realworld applications, such as traffic surveillance elements, online retailing, automatic driving systems, fashion and apparel industry, et al., where the information in these applications usually comes through multimedia data. For example, the videos in traffic surveillance contain both acoustic and visual signals; the online product in online retailing app or website include both image, video, text and even acoustic information; the sensory perceptions typically used in automatic driving system may need extensive multi-media data from multichannel inputs in visual, auditory and motor pathways. Thereby, how to characterize the property of multimedia data so that it can be managed to enable different learning tasks of various applications in consumer electronics, is of great importance. This requires researchers to develop robust models to classify, retrieve and understand multi-media information in these real-world applications.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 2","pages":"4961-4964"},"PeriodicalIF":4.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-29DOI: 10.1109/TCE.2024.3409489
{"title":"IEEE Consumer Technology Society Officers and Committee Chairs","authors":"","doi":"10.1109/TCE.2024.3409489","DOIUrl":"https://doi.org/10.1109/TCE.2024.3409489","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 2","pages":"C4-C4"},"PeriodicalIF":4.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-29DOI: 10.1109/TCE.2024.3386369
Mingbo Zhao;Weizhi Meng;Bingyi Liu;Yimin Yang
Consumer electronics are electronic equipment intended for everyday use, and they constitute a part of the wider electronics industry including devices and services used for entertainment, communications and recreation. In practice, consumer electronics use digital technologies to enhance performance in real-world applications, such as AI-generated content, chatbot, online retailing, automatic driving systems, fashion and apparel industry, etc., where the information in these applications usually generate a significant amount of high-quality data for creation of digital content, Semantic Comprehension or data generation and augmentation, etc. Recently, Generative Artificial Intelligence (GAI) has been gaining significant attention from society. For example, ChatGPT is a language model developed by OpenAI for building chatbot, which can efficiently understand and respond to human language in a logical and meaningful way. In addition, DALL-E-2 is another state-of-the-art GAI model, which is capable of creating unique and high-quality images from textual descriptions in a few minutes. In general, GAI techniques, as opposed to being created by human authors, is to automate the creation of large amounts of content such as images, music, and natural language, etc. Therefore, how to develop robust models for generative AI in this field of consumer electronics is of great importance.
消费电子产品是供日常使用的电子设备，是更广泛的电子产业的一部分，包括用于娱乐、通信和休闲的设备和服务。在实践中，消费电子产品利用数字技术来增强现实世界应用中的性能，如人工智能生成的内容、聊天机器人、在线零售、自动驾驶系统、时尚和服装行业等，这些应用中的信息通常会产生大量高质量数据，用于创建数字内容、语义理解或数据生成和增强等。最近，生成式人工智能（GAI）受到了社会的广泛关注。例如，ChatGPT 是 OpenAI 为构建聊天机器人而开发的一种语言模型，它可以高效地理解人类语言，并对其做出有逻辑、有意义的回应。此外，DALL-E-2 是另一种最先进的 GAI 模型，它能够在几分钟内根据文本描述创建独特而高质量的图像。一般来说，GAI 技术与由人类作者创建的技术不同，它可以自动创建大量内容，如图像、音乐和自然语言等。因此，如何在消费电子产品这一领域为生成式人工智能开发稳健的模型就显得尤为重要。
{"title":"Guest Editorial of the Special Section on Generative Artificial Intelligence With Applications on Consumer Electronics","authors":"Mingbo Zhao;Weizhi Meng;Bingyi Liu;Yimin Yang","doi":"10.1109/TCE.2024.3386369","DOIUrl":"https://doi.org/10.1109/TCE.2024.3386369","url":null,"abstract":"Consumer electronics are electronic equipment intended for everyday use, and they constitute a part of the wider electronics industry including devices and services used for entertainment, communications and recreation. In practice, consumer electronics use digital technologies to enhance performance in real-world applications, such as AI-generated content, chatbot, online retailing, automatic driving systems, fashion and apparel industry, etc., where the information in these applications usually generate a significant amount of high-quality data for creation of digital content, Semantic Comprehension or data generation and augmentation, etc. Recently, Generative Artificial Intelligence (GAI) has been gaining significant attention from society. For example, ChatGPT is a language model developed by OpenAI for building chatbot, which can efficiently understand and respond to human language in a logical and meaningful way. In addition, DALL-E-2 is another state-of-the-art GAI model, which is capable of creating unique and high-quality images from textual descriptions in a few minutes. In general, GAI techniques, as opposed to being created by human authors, is to automate the creation of large amounts of content such as images, music, and natural language, etc. Therefore, how to develop robust models for generative AI in this field of consumer electronics is of great importance.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 2","pages":"4955-4957"},"PeriodicalIF":4.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-29DOI: 10.1109/TCE.2024.3407968
Amit Kumar Singh;Stefano Berretti
This is an era of the prevalence of multimedia, an era of a community of shared future in cyberspace. In the last few years, the number of available multimedia and multimodal applications increased at a fastest step also thanks to the availability of several multimedia editing tools. This has opened the way to more powerful and human-friendly devices and applications that use multimedia data. As Internet technology continues to evolve, the sharing of multimedia information between people has become more frequent, but it has also exacerbated privacy risks. Further, Internet of Multimedia Things (IoMT), which are hidden inside everyday multimedia objects that surround us and help us in many smart applications, are becoming the dominant technologies in tele-healthcare, homes, mobile phones, intelligent devices and instruments, and consumer multimedia electronic. To make modern life easier, consumer electronic equipment is widely used to transmit multimedia information in many fields, such as academia, social media, healthcare, business, and industry. However, secure multimedia and multimodal data transmission are new challenges for many professionals and researchers. Therefore, it is of great research significance and application value to design and propose a secure system for multimedia data for consumer’s electronic applications. The guest editorial team believes that the articles included in this special section will be convenient security and privacy solutions of multimedia and multimodal data for consumer’s electronic applications.
{"title":"Guest Editorial Security and Privacy of Multimedia and Multimodal Data for Consumers Electronic Applications","authors":"Amit Kumar Singh;Stefano Berretti","doi":"10.1109/TCE.2024.3407968","DOIUrl":"https://doi.org/10.1109/TCE.2024.3407968","url":null,"abstract":"This is an era of the prevalence of multimedia, an era of a community of shared future in cyberspace. In the last few years, the number of available multimedia and multimodal applications increased at a fastest step also thanks to the availability of several multimedia editing tools. This has opened the way to more powerful and human-friendly devices and applications that use multimedia data. As Internet technology continues to evolve, the sharing of multimedia information between people has become more frequent, but it has also exacerbated privacy risks. Further, Internet of Multimedia Things (IoMT), which are hidden inside everyday multimedia objects that surround us and help us in many smart applications, are becoming the dominant technologies in tele-healthcare, homes, mobile phones, intelligent devices and instruments, and consumer multimedia electronic. To make modern life easier, consumer electronic equipment is widely used to transmit multimedia information in many fields, such as academia, social media, healthcare, business, and industry. However, secure multimedia and multimodal data transmission are new challenges for many professionals and researchers. Therefore, it is of great research significance and application value to design and propose a secure system for multimedia data for consumer’s electronic applications. The guest editorial team believes that the articles included in this special section will be convenient security and privacy solutions of multimedia and multimodal data for consumer’s electronic applications.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 2","pages":"4908-4909"},"PeriodicalIF":4.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-29DOI: 10.1109/TCE.2024.3409490
{"title":"IEEE Consumer Technology Society Board of Governors","authors":"","doi":"10.1109/TCE.2024.3409490","DOIUrl":"https://doi.org/10.1109/TCE.2024.3409490","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 2","pages":"C3-C3"},"PeriodicalIF":4.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659253","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-29DOI: 10.1109/TCE.2024.3383608
Arun Kumar Sangaiah;Xizhao Wang;Mohammad S. Obaidat;Patrick C. K. Huang;Kannan Govindan
In recent years, the integration of advanced technologies such as communication advancements (e.g., 5G), Artificial Intelligence (AI), industrial edge computing, and adversarial Machine Learning (ML) has accelerated the evolution of Industry 5.0 systems, shaping digital ecosystems for consumers. This convergence of technologies holds promise for addressing the service requirements and cybersecurity strategies essential for Industry 5.0 systems within digital ecosystems. Industry 5.0, the fifth industrial revolution, represents a paradigm shift integrating digital ecosystems and emerging technologies like the Internet of Things (IoT), Cyber-Physical Systems (CPS), cloud computing, and AI. These technologies converge to establish intelligent, open, and secure factories, revolutionizing industrial automation and manufacturing processes.
{"title":"Guest Editorial Data-Driven Innovation and Adversarial Learning Models for Industry 5.0 Toward Consumer Digital Ecosystems","authors":"Arun Kumar Sangaiah;Xizhao Wang;Mohammad S. Obaidat;Patrick C. K. Huang;Kannan Govindan","doi":"10.1109/TCE.2024.3383608","DOIUrl":"https://doi.org/10.1109/TCE.2024.3383608","url":null,"abstract":"In recent years, the integration of advanced technologies such as communication advancements (e.g., 5G), Artificial Intelligence (AI), industrial edge computing, and adversarial Machine Learning (ML) has accelerated the evolution of Industry 5.0 systems, shaping digital ecosystems for consumers. This convergence of technologies holds promise for addressing the service requirements and cybersecurity strategies essential for Industry 5.0 systems within digital ecosystems. Industry 5.0, the fifth industrial revolution, represents a paradigm shift integrating digital ecosystems and emerging technologies like the Internet of Things (IoT), Cyber-Physical Systems (CPS), cloud computing, and AI. These technologies converge to establish intelligent, open, and secure factories, revolutionizing industrial automation and manufacturing processes.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 2","pages":"4878-4881"},"PeriodicalIF":4.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659336","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-27DOI: 10.1109/tce.2024.3450649
Jingwei Liu, Yufeng Wu, Wei Du, Rong Sun, Guangxia Xu, Lei Liu, Celimuge Wu
{"title":"Byzantine-Robust Hierarchical Aggregation for Cross-Device Federated Learning in Consumer IoT","authors":"Jingwei Liu, Yufeng Wu, Wei Du, Rong Sun, Guangxia Xu, Lei Liu, Celimuge Wu","doi":"10.1109/tce.2024.3450649","DOIUrl":"https://doi.org/10.1109/tce.2024.3450649","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"1 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189665","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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