Edge computing for Internet of Everything : a survey
- Kong, Xiangjie, Wu, Yuhan, Wang, Hui, Xia, Feng
- Authors: Kong, Xiangjie , Wu, Yuhan , Wang, Hui , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 9, no. 23 (2022), p. 23472-23485
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- Description: In this era of the Internet of Everything (IoE), edge computing has emerged as the critical enabling technology to solve a series of issues caused by an increasing amount of interconnected devices and large-scale data transmission. However, the deficiencies of edge computing paradigm are gradually being magnified in the context of IoE, especially in terms of service migration, security and privacy preservation, and deployment issues of edge node. These issues can not be well addressed by conventional approaches. Thanks to the rapid development of upcoming technologies, such as artificial intelligence (AI), blockchain, and microservices, novel and more effective solutions have emerged and been applied to solve existing challenges. In addition, edge computing can be deeply integrated with technologies in other domains (e.g., AI, blockchain, 6G, and digital twin) through interdisciplinary intersection and practice, releasing the potential for mutual benefit. These promising integrations need to be further explored and researched. In addition, edge computing provides strong support in applications scenarios, such as remote working, new physical retail industries, and digital advertising, which has greatly changed the way we live, work, and study. In this article, we present an up-to-date survey of the edge computing research. In addition to introducing the definition, model, and characteristics of edge computing, we discuss a set of key issues in edge computing and novel solutions supported by emerging technologies in IoE era. Furthermore, we explore the potential and promising trends from the perspective of technology integration. Finally, new application scenarios and the final form of edge computing are discussed. © 2014 IEEE.
- Authors: Kong, Xiangjie , Wu, Yuhan , Wang, Hui , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 9, no. 23 (2022), p. 23472-23485
- Full Text:
- Reviewed:
- Description: In this era of the Internet of Everything (IoE), edge computing has emerged as the critical enabling technology to solve a series of issues caused by an increasing amount of interconnected devices and large-scale data transmission. However, the deficiencies of edge computing paradigm are gradually being magnified in the context of IoE, especially in terms of service migration, security and privacy preservation, and deployment issues of edge node. These issues can not be well addressed by conventional approaches. Thanks to the rapid development of upcoming technologies, such as artificial intelligence (AI), blockchain, and microservices, novel and more effective solutions have emerged and been applied to solve existing challenges. In addition, edge computing can be deeply integrated with technologies in other domains (e.g., AI, blockchain, 6G, and digital twin) through interdisciplinary intersection and practice, releasing the potential for mutual benefit. These promising integrations need to be further explored and researched. In addition, edge computing provides strong support in applications scenarios, such as remote working, new physical retail industries, and digital advertising, which has greatly changed the way we live, work, and study. In this article, we present an up-to-date survey of the edge computing research. In addition to introducing the definition, model, and characteristics of edge computing, we discuss a set of key issues in edge computing and novel solutions supported by emerging technologies in IoE era. Furthermore, we explore the potential and promising trends from the perspective of technology integration. Finally, new application scenarios and the final form of edge computing are discussed. © 2014 IEEE.
Multimodal educational data fusion for students' mental health detection
- Guo, Teng, Zhao, Wenhong, Alrashoud, Mubarak, Tolba, Amr, Firmin, Sally, Xia, Feng
- Authors: Guo, Teng , Zhao, Wenhong , Alrashoud, Mubarak , Tolba, Amr , Firmin, Sally , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 70370-70382
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- Description: Mental health issues can lead to serious consequences like depression, self-mutilation, and worse, especially for university students who are not physically and mentally mature. Not all students with poor mental health are aware of their situation and actively seek help. Proactive detection of mental problems is a critical step in addressing this issue. However, accurate detections are hard to achieve due to the inherent complexity and heterogeneity of unstructured multi-modal data generated by campus life. Against this background, we propose a detection framework for detecting students' mental health, named CASTLE (educational data fusion for mental health detection). Three parts are involved in this framework. First, we utilize representation learning to fuse data on social life, academic performance, and physical appearance. An algorithm, named MOON (multi-view social network embedding), is proposed to represent students' social life in a comprehensive way by fusing students' heterogeneous social relations effectively. Second, a synthetic minority oversampling technique algorithm (SMOTE) is applied to the label imbalance issue. Finally, a DNN (deep neural network) model is utilized for the final detection. The extensive results demonstrate the promising performance of the proposed methods in comparison to an extensive range of state-of-the-art baselines. © 2013 IEEE.
- Authors: Guo, Teng , Zhao, Wenhong , Alrashoud, Mubarak , Tolba, Amr , Firmin, Sally , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 70370-70382
- Full Text:
- Reviewed:
- Description: Mental health issues can lead to serious consequences like depression, self-mutilation, and worse, especially for university students who are not physically and mentally mature. Not all students with poor mental health are aware of their situation and actively seek help. Proactive detection of mental problems is a critical step in addressing this issue. However, accurate detections are hard to achieve due to the inherent complexity and heterogeneity of unstructured multi-modal data generated by campus life. Against this background, we propose a detection framework for detecting students' mental health, named CASTLE (educational data fusion for mental health detection). Three parts are involved in this framework. First, we utilize representation learning to fuse data on social life, academic performance, and physical appearance. An algorithm, named MOON (multi-view social network embedding), is proposed to represent students' social life in a comprehensive way by fusing students' heterogeneous social relations effectively. Second, a synthetic minority oversampling technique algorithm (SMOTE) is applied to the label imbalance issue. Finally, a DNN (deep neural network) model is utilized for the final detection. The extensive results demonstrate the promising performance of the proposed methods in comparison to an extensive range of state-of-the-art baselines. © 2013 IEEE.
MESH : a flexible manifold-embedded semantic hashing for cross-modal retrieval
- Zhong, Fangming, Wang, Guangze, Chen, Zhikui, Xia, Feng
- Authors: Zhong, Fangming , Wang, Guangze , Chen, Zhikui , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 147569-147579
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- Description: Hashing based methods for cross-modal retrieval has been widely explored in recent years. However, most of them mainly focus on the preservation of neighborhood relationship and label consistency, while ignore the proximity of neighbors and proximity of classes, which degrades the discrimination of hash codes. And most of them learn hash codes and hashing functions simultaneously, which limits the flexibility of algorithms. To address these issues, in this article, we propose a two-step cross-modal retrieval method named Manifold-Embedded Semantic Hashing (MESH). It exploits Local Linear Embedding to model the neighborhood proximity and uses class semantic embeddings to consider the proximity of classes. By so doing, MESH can not only extract the manifold structure in different modalities, but also can embed the class semantic information into hash codes to further improve the discrimination of learned hash codes. Moreover, the two-step scheme makes MESH flexible to various hashing functions. Extensive experimental results on three datasets show that MESH is superior to 10 state-of-the-art cross-modal hashing methods. Moreover, MESH also demonstrates superiority on deep features compared with the deep cross-modal hashing method. © 2013 IEEE.
- Authors: Zhong, Fangming , Wang, Guangze , Chen, Zhikui , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 147569-147579
- Full Text:
- Reviewed:
- Description: Hashing based methods for cross-modal retrieval has been widely explored in recent years. However, most of them mainly focus on the preservation of neighborhood relationship and label consistency, while ignore the proximity of neighbors and proximity of classes, which degrades the discrimination of hash codes. And most of them learn hash codes and hashing functions simultaneously, which limits the flexibility of algorithms. To address these issues, in this article, we propose a two-step cross-modal retrieval method named Manifold-Embedded Semantic Hashing (MESH). It exploits Local Linear Embedding to model the neighborhood proximity and uses class semantic embeddings to consider the proximity of classes. By so doing, MESH can not only extract the manifold structure in different modalities, but also can embed the class semantic information into hash codes to further improve the discrimination of learned hash codes. Moreover, the two-step scheme makes MESH flexible to various hashing functions. Extensive experimental results on three datasets show that MESH is superior to 10 state-of-the-art cross-modal hashing methods. Moreover, MESH also demonstrates superiority on deep features compared with the deep cross-modal hashing method. © 2013 IEEE.
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