The Experts below are selected from a list of 83982 Experts worldwide ranked by ideXlab platform
Wen Gao - One of the best experts on this subject based on the ideXlab platform.
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Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Yihang Lou, Ling-yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen GaoAbstract:The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner. By deploying deep learning and/or handcrafted models in front-end devices, the compact features can be extracted and subsequently delivered to back-end cloud for search and advanced analytics. In this context, we propose a model generation, utilization, and Communication Paradigm, aiming to address a set of unique challenges for better artificial intelligence services in smart cities. In particular, we present an integrated multiple deep learning models reuse and prediction strategy, which greatly increases the feasibility of the digital retina in processing and analyzing the large-scale visual data in smart cities. The promise of the proposed Paradigm is demonstrated through a set of experiments.
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Toward Knowledge as a Service Over Networks: A Deep Learning Model Communication Paradigm
IEEE Journal on Selected Areas in Communications, 2019Co-Authors: Ziqian Chen, Yihang Lou, Ling-yu Duan, Shiqi Wang, Tiejun Huang, Dapeng Oliver Wu, Wen GaoAbstract:The advent of artificial intelligence and Internet of Things has led to the seamless transition turning the big data into the big knowledge. The deep learning models, which assimilate knowledge from large-scale data, can be regarded as an alternative but promising modality of knowledge for artificial intelligence services. Yet, the compression, storage, and Communication of the deep learning models towards better knowledge services, especially over networks, pose a set of challenging problems on both industrial and academic realms. This paper presents the deep learning model Communication Paradigm based on multiple model compression, which greatly exploits the redundancy among multiple deep learning models in different application scenarios. We analyze the potential and demonstrate the promise of the compression strategy for deep learning model Communication through a set of experiments. Moreover, the interoperability in deep learning model Communication, which is enabled based on the standardization of compact deep learning model representation, is also discussed and envisioned.
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ICME - Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
2019 IEEE International Conference on Multimedia and Expo (ICME), 2019Co-Authors: Yihang Lou, Ling-yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen GaoAbstract:The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner. By deploying deep learning and/or handcrafted models in front-end devices, the compact features can be extracted and subsequently delivered to back-end cloud for search and advanced analytics. In this context, we propose a model generation, utilization, and Communication Paradigm, aiming to address a set of unique challenges for better artificial intelligence services in smart cities. In particular, we present an integrated multiple deep learning models reuse and prediction strategy, which greatly increases the feasibility of the digital retina in processing and analyzing the large-scale visual data in smart cities. The promise of the proposed Paradigm is demonstrated through a set of experiments.
Yihang Lou - One of the best experts on this subject based on the ideXlab platform.
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Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Yihang Lou, Ling-yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen GaoAbstract:The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner. By deploying deep learning and/or handcrafted models in front-end devices, the compact features can be extracted and subsequently delivered to back-end cloud for search and advanced analytics. In this context, we propose a model generation, utilization, and Communication Paradigm, aiming to address a set of unique challenges for better artificial intelligence services in smart cities. In particular, we present an integrated multiple deep learning models reuse and prediction strategy, which greatly increases the feasibility of the digital retina in processing and analyzing the large-scale visual data in smart cities. The promise of the proposed Paradigm is demonstrated through a set of experiments.
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Toward Knowledge as a Service Over Networks: A Deep Learning Model Communication Paradigm
IEEE Journal on Selected Areas in Communications, 2019Co-Authors: Ziqian Chen, Yihang Lou, Ling-yu Duan, Shiqi Wang, Tiejun Huang, Dapeng Oliver Wu, Wen GaoAbstract:The advent of artificial intelligence and Internet of Things has led to the seamless transition turning the big data into the big knowledge. The deep learning models, which assimilate knowledge from large-scale data, can be regarded as an alternative but promising modality of knowledge for artificial intelligence services. Yet, the compression, storage, and Communication of the deep learning models towards better knowledge services, especially over networks, pose a set of challenging problems on both industrial and academic realms. This paper presents the deep learning model Communication Paradigm based on multiple model compression, which greatly exploits the redundancy among multiple deep learning models in different application scenarios. We analyze the potential and demonstrate the promise of the compression strategy for deep learning model Communication through a set of experiments. Moreover, the interoperability in deep learning model Communication, which is enabled based on the standardization of compact deep learning model representation, is also discussed and envisioned.
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ICME - Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
2019 IEEE International Conference on Multimedia and Expo (ICME), 2019Co-Authors: Yihang Lou, Ling-yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen GaoAbstract:The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner. By deploying deep learning and/or handcrafted models in front-end devices, the compact features can be extracted and subsequently delivered to back-end cloud for search and advanced analytics. In this context, we propose a model generation, utilization, and Communication Paradigm, aiming to address a set of unique challenges for better artificial intelligence services in smart cities. In particular, we present an integrated multiple deep learning models reuse and prediction strategy, which greatly increases the feasibility of the digital retina in processing and analyzing the large-scale visual data in smart cities. The promise of the proposed Paradigm is demonstrated through a set of experiments.
Rivièreetienne - One of the best experts on this subject based on the ideXlab platform.
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Confidentiality-Preserving Publish/Subscribe
ACM Computing Surveys, 2016Co-Authors: Onicaemanuel, Felberpascal, Mercierhugues, RivièreetienneAbstract:Publish/subscribe (pub/sub) is an attractive Communication Paradigm for large-scale distributed applications running across multiple administrative domains. Pub/sub allows event-based information d...
Antonio Fernandez Gómez-skarmeta - One of the best experts on this subject based on the ideXlab platform.
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Experimental evaluation of a novel vehicular Communication Paradigm based on cellular networks
2008 IEEE Intelligent Vehicles Symposium, 2008Co-Authors: Jose Santa, Antonio Moragon, Antonio Fernandez Gómez-skarmetaAbstract:In the field of vehicular networks, the amount of telematic services which are usually taken into account is very limited. Safety services, and specifically collision avoidance applications, practically receive an exclusive attention. Due to vehicular ad-hoc networks (VANETs) are the most used Communication technology, services conceived for the vehicle domain are frequently designed to take advantage of its benefits, but also to suffer its limitations. The intention of this paper is proposing a novel Communication Paradigm open to the development of any vehicular service with connectivity requirements. This way, not only vehicle to vehicle (V2V) necessities are considered, but also vehicle to infrastructure (V2I) connections are taken into account with the same importance. The work presented here chooses the cellular networks as a valid alternative to VANET approaches in most of the cases, with the added value of V2I capabilities. A design based on peer to peer (P2P) networks has been implemented and tested over a real environment. The hardware/software prototype is explained and main performance measurements prove our system is a feasible Communication Paradigm for most of vehicular services.
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A novel vehicle Communication Paradigm based on Cellular Networks for improving the safety in roads
International Journal of Intelligent Information and Database Systems, 2008Co-Authors: Jose Santa, Rafael Toledo-moreo, Antonio Fernandez Gómez-skarmetaAbstract:Main aim of Intelligent Transport Systems (ITS) applied to roads is to increase their safety. To achieve this goal, many researchers are focused on developing robust and efficient Communication links between vehicle to infrastructure (V2I) and Vehicle to Vehicle (V2V). Most of the works in the current literature are based on vehicular ad hoc networks. This paper presents a network infrastructure based on Cellular Networks (CNs) and Peer to Peer (P2P) technologies, to develop a Communication Paradigm for improving road safety, and integrating both V2I and V2V in one design. For localisation purposes, a Global Navigation Satellite Systems (GNSS) is used. Since we are dealing with safety applications, a very special emphasis on the integrity of the positioning is put, and an integrity parameter which measures it is used to improve the location of road incidences. Details of the prototype developed and the tests performed in a real environment are given.
Ziqian Chen - One of the best experts on this subject based on the ideXlab platform.
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Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Yihang Lou, Ling-yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen GaoAbstract:The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner. By deploying deep learning and/or handcrafted models in front-end devices, the compact features can be extracted and subsequently delivered to back-end cloud for search and advanced analytics. In this context, we propose a model generation, utilization, and Communication Paradigm, aiming to address a set of unique challenges for better artificial intelligence services in smart cities. In particular, we present an integrated multiple deep learning models reuse and prediction strategy, which greatly increases the feasibility of the digital retina in processing and analyzing the large-scale visual data in smart cities. The promise of the proposed Paradigm is demonstrated through a set of experiments.
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Toward Knowledge as a Service Over Networks: A Deep Learning Model Communication Paradigm
IEEE Journal on Selected Areas in Communications, 2019Co-Authors: Ziqian Chen, Yihang Lou, Ling-yu Duan, Shiqi Wang, Tiejun Huang, Dapeng Oliver Wu, Wen GaoAbstract:The advent of artificial intelligence and Internet of Things has led to the seamless transition turning the big data into the big knowledge. The deep learning models, which assimilate knowledge from large-scale data, can be regarded as an alternative but promising modality of knowledge for artificial intelligence services. Yet, the compression, storage, and Communication of the deep learning models towards better knowledge services, especially over networks, pose a set of challenging problems on both industrial and academic realms. This paper presents the deep learning model Communication Paradigm based on multiple model compression, which greatly exploits the redundancy among multiple deep learning models in different application scenarios. We analyze the potential and demonstrate the promise of the compression strategy for deep learning model Communication through a set of experiments. Moreover, the interoperability in deep learning model Communication, which is enabled based on the standardization of compact deep learning model representation, is also discussed and envisioned.
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ICME - Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
2019 IEEE International Conference on Multimedia and Expo (ICME), 2019Co-Authors: Yihang Lou, Ling-yu Duan, Yong Luo, Ziqian Chen, Tongliang Liu, Shiqi Wang, Wen GaoAbstract:The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner. By deploying deep learning and/or handcrafted models in front-end devices, the compact features can be extracted and subsequently delivered to back-end cloud for search and advanced analytics. In this context, we propose a model generation, utilization, and Communication Paradigm, aiming to address a set of unique challenges for better artificial intelligence services in smart cities. In particular, we present an integrated multiple deep learning models reuse and prediction strategy, which greatly increases the feasibility of the digital retina in processing and analyzing the large-scale visual data in smart cities. The promise of the proposed Paradigm is demonstrated through a set of experiments.