In the future development direction of the sixth generation(6G)mobile communication,several communication models are proposed to face the growing challenges of the task.The rapid development of artificial intelligence...In the future development direction of the sixth generation(6G)mobile communication,several communication models are proposed to face the growing challenges of the task.The rapid development of artificial intelligence(AI)foundation models provides significant support for efficient and intelligent communication interactions.In this paper,we propose an innovative semantic communication paradigm called task-oriented semantic communication system with foundation models.First,we segment the image by using task prompts based on the segment anything model(SAM)and contrastive language-image pretraining(CLIP).Meanwhile,we adopt Bezier curve to enhance the mask to improve the segmentation accuracy.Second,we have differentiated semantic compression and transmission approaches for segmented content.Third,we fuse different semantic information based on the conditional diffusion model to generate high-quality images that satisfy the users'specific task requirements.Finally,the experimental results show that the proposed system compresses the semantic information effectively and improves the robustness of semantic communication.展开更多
To facilitate emerging applications and demands of edge intelligence(EI)-empowered 6G networks,model-driven semantic communications have been proposed to reduce transmission volume by deploying artificial intelligence...To facilitate emerging applications and demands of edge intelligence(EI)-empowered 6G networks,model-driven semantic communications have been proposed to reduce transmission volume by deploying artificial intelligence(AI)models that provide abilities of semantic extraction and recovery.Nevertheless,it is not feasible to preload all AI models on resource-constrained terminals.Thus,in-time model transmission becomes a crucial problem.This paper proposes an intellicise model transmission architecture to guarantee the reliable transmission of models for semantic communication.The mathematical relationship between model size and performance is formulated by employing a recognition error function supported with experimental data.We consider the characteristics of wireless channels and derive the closed-form expression of model transmission outage probability(MTOP)over the Rayleigh channel.Besides,we define the effective model accuracy(EMA)to evaluate the model transmission performance of both communication and intelligence.Then we propose a joint model selection and resource allocation(JMSRA)algorithm to maximize the average EMA of all users.Simulation results demonstrate that the average EMA of the JMSRA algorithm outperforms baseline algorithms by about 22%.展开更多
Video transmission requires considerable bandwidth,and current widely employed schemes prove inadequate when confronted with scenes featuring prominently.Motivated by the strides in talkinghead generative technology,t...Video transmission requires considerable bandwidth,and current widely employed schemes prove inadequate when confronted with scenes featuring prominently.Motivated by the strides in talkinghead generative technology,the paper introduces a semantic transmission system tailored for talking-head videos.The system captures semantic information from talking-head video and faithfully reconstructs source video at the receiver,only one-shot reference frame and compact semantic features are required for the entire transmission.Specifically,we analyze video semantics in the pixel domain frame-by-frame and jointly process multi-frame semantic information to seamlessly incorporate spatial and temporal information.Variational modeling is utilized to evaluate the diversity of importance among group semantics,thereby guiding bandwidth resource allocation for semantics to enhance system efficiency.The whole endto-end system is modeled as an optimization problem and equivalent to acquiring optimal rate-distortion performance.We evaluate our system on both reference frame and video transmission,experimental results demonstrate that our system can improve the efficiency and robustness of communications.Compared to the classical approaches,our system can save over 90%of bandwidth when user perception is close.展开更多
As a novel paradigm,semantic communication provides an effective solution for breaking through the future development dilemma of classical communication systems.However,it remains an unsolved problem of how to measure...As a novel paradigm,semantic communication provides an effective solution for breaking through the future development dilemma of classical communication systems.However,it remains an unsolved problem of how to measure the information transmission capability for a given semantic communication method and subsequently compare it with the classical communication method.In this paper,we first present a review of the semantic communication system,including its system model and the two typical coding and transmission methods for its implementations.To address the unsolved issue of the information transmission capability measure for semantic communication methods,we propose a new universal performance measure called Information Conductivity.We provide the definition and the physical significance to state its effectiveness in representing the information transmission capabilities of the semantic communication systems and present elaborations including its measure methods,degrees of freedom,and progressive analysis.Experimental results in image transmission scenarios validate its practical applicability.展开更多
Purpose:This study attempts to propose an abstract model by gathering concepts that can focus on resource representation and description in a digital curation model and suggest a conceptual model that emphasizes seman...Purpose:This study attempts to propose an abstract model by gathering concepts that can focus on resource representation and description in a digital curation model and suggest a conceptual model that emphasizes semantic enrichment in a digital curation model.Design/methodology/approach:This study conducts a literature review to analyze the preceding curation models,DCC CLM,DCC&U,UC3,and DCN.Findings:The concept of semantic enrichment is expressed in a single word,SEMANTIC in this study.The Semantic Enrichment Model,SEMANTIC has elements,subject,extraction,multi-language,authority,network,thing,identity,and connect.Research limitations:This study does not reflect the actual information environment because it focuses on the concepts of the representation of digital objects.Practical implications:This study presents the main considerations for creating and reinforcing the description and representation of digital objects when building and developing digital curation models in specific institutions.Originality/value:This study summarizes the elements that should be emphasized in the representation of digital objects in terms of information organization.展开更多
A new method is proposed for constructing the Chinese sentential semantic structure in this paper. The method adopts the features including predicates, relations between predicates and basic arguments, relations betwe...A new method is proposed for constructing the Chinese sentential semantic structure in this paper. The method adopts the features including predicates, relations between predicates and basic arguments, relations between words, and case types to train the models of CRF ＋ ＋ and de- pendency parser. On the basis of the data set in Beijing Forest Studio-Chinese Tagged Corpus （ BFS- CTC）, the proposed method obtains precision value of 73.63% in open test. This result shows that the formalized computer processing can construct the sentential semantic structure absolutely. The features of predicates, topic and comment extracted with the method can be applied in Chinese in- formation processing directly for promoting the development of Chinese semantic analysis. The method makes the analysis of sentential semantic analysis based on large scale of data possible. It is a tool for expanding the corpus and has certain theoretical research and practical application value.展开更多
With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available fro...With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.展开更多
Cloud storage has the characteristics of distributed and virtual, and it makes the ownership rights and management rights of users data separated. The master-slave architecture of cloud storage has a problem of single...Cloud storage has the characteristics of distributed and virtual, and it makes the ownership rights and management rights of users data separated. The master-slave architecture of cloud storage has a problem of single point failure. In this paper, we provide a cloud storage architecture model based on Semantic equivalence. According to semantic matching degree, this architecture divides the nodes into node cluster by creating semantic tree and maintains system routing through semantic hypergraph. Through simulation experiments show that dividing network into semantic can enhance scalability and flexibility of the system, and it can improve the efficiency of network organization and the security of cloud storage system, at the same time, it can also reduce the cloud data storage and the delay of reading time.展开更多
Wireless smart home system is to facilitate people's lives and it trend to adopt a more intelligent way to provide services. It is very desirable in the recent SH market for the system to recognize users' beha...Wireless smart home system is to facilitate people's lives and it trend to adopt a more intelligent way to provide services. It is very desirable in the recent SH market for the system to recognize users' behaviors and automatically response the corresponding activities to satisfy users' actual demands. However, activity models in the existing approaches are usually defined separately through knowledge-driven methods. These approaches cause that the activity models can't be matched with the services dynamically. To address the problem, we develop the semantic association model and a novel approach of activity recognition and guidance is presented. In our approach, the smart devices and users' requirements are described by semantic models. When the requirements are detected and understood, smart gateway can provide appropriate services, achieving activity assistance. The semantic association model allows all related elements in smart home connect with each other logically. The approach has been implemented and the results show that the success rate of the approach based on semantic association model is higher than 33% at average as compared to the approach based on predefined models. The proposed approach can effectively help people who are in trouble with learning or remembering in the common life.展开更多
Learning-based multi-task models have been widely used in various scene understanding tasks,and complement each other,i.e.,they allow us to consider prior semantic information to better infer depth.We boost the unsupe...Learning-based multi-task models have been widely used in various scene understanding tasks,and complement each other,i.e.,they allow us to consider prior semantic information to better infer depth.We boost the unsupervised monocular depth estimation using semantic segmentation as an auxiliary task.To address the lack of cross-domain datasets and catastrophic forgetting problems encountered in multi-task training,we utilize existing methodology to obtain redundant segmentation maps to build our cross-domain dataset,which not only provides a new way to conduct multi-task training,but also helps us to evaluate results compared with those of other algorithms.In addition,in order to comprehensively use the extracted features of the two tasks in the early perception stage,we use a strategy of sharing weights in the network to fuse cross-domain features,and introduce a novel multi-task loss function to further smooth the depth values.Extensive experiments on KITTI and Cityscapes datasets show that our method has achieved state-of-the-art performance in the depth estimation task,as well improved semantic segmentation.展开更多
随着移动通信技术的发展演进,6G(6th-Generation)网络作为新一代智能化数字信息基础设施,将不再仅聚焦信号的传输和复现,更需要基于电磁传播过程实现对周围环境的高效感知和理解,从而获取信道语义知识,协助智能通信体的预测、决策、波...随着移动通信技术的发展演进,6G(6th-Generation)网络作为新一代智能化数字信息基础设施,将不再仅聚焦信号的传输和复现,更需要基于电磁传播过程实现对周围环境的高效感知和理解,从而获取信道语义知识,协助智能通信体的预测、决策、波束成形等.因此,相较于传统信道而言,赋予无线信道模型对物理环境的语义理解、重构、表达能力,已成为智能无线信道模型的重要特征.本文提出了一种无线信道语义的分析和建模方法,将信道语义定义为状态语义、行为语义和事件语义3种层级,分别对应信道瞬态多径、信道时变轨迹和信道拓扑结构.此外,基于车载通感一体化(Integrated Sensing And Communication,ISAC)信道测量系统,开展了28 GHz下面向信道语义表征的无线信道测量,基于实测数据对信道语义进行解构、标识、建模,重点分析了3种不同语义下的信道多径分布特性,完成了语义导向的信道生成,结果表明信道语义模型能够在生成较准确信道的同时,表达更丰富的语义信息.本文工作是在语义层面上探索智能信道建模的新方法,通过深入挖掘无线信道的内在语义特征,促进通信系统在理解和认知环境方面的能力,从而提高通信效率和质量.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant(62001246,62231017,62201277,62071255)the Natural Science Foundation of Jiangsu Province under Grant BK20220390+3 种基金Key R and D Program of Jiangsu Province Key project and topics under Grant(BE2021095,BE2023035)the Natural Science Research Startup Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(Grant No.NY221011)National Science Foundation of Xiamen,China(No.3502Z202372013)Open Project of the Key Laboratory of Underwater Acoustic Communication and Marine Information Technology(Xiamen University)of the Ministry of Education,China(No.UAC202304)。
文摘In the future development direction of the sixth generation(6G)mobile communication,several communication models are proposed to face the growing challenges of the task.The rapid development of artificial intelligence(AI)foundation models provides significant support for efficient and intelligent communication interactions.In this paper,we propose an innovative semantic communication paradigm called task-oriented semantic communication system with foundation models.First,we segment the image by using task prompts based on the segment anything model(SAM)and contrastive language-image pretraining(CLIP).Meanwhile,we adopt Bezier curve to enhance the mask to improve the segmentation accuracy.Second,we have differentiated semantic compression and transmission approaches for segmented content.Third,we fuse different semantic information based on the conditional diffusion model to generate high-quality images that satisfy the users'specific task requirements.Finally,the experimental results show that the proposed system compresses the semantic information effectively and improves the robustness of semantic communication.
基金supported in part by the National Key R&D Program of China No.2020YFB1806905the National Natural Science Foundation of China No.62201079+1 种基金the Beijing Natural Science Foundation No.L232051the Major Key Project of Peng Cheng Laboratory(PCL)Department of Broadband Communication。
文摘To facilitate emerging applications and demands of edge intelligence(EI)-empowered 6G networks,model-driven semantic communications have been proposed to reduce transmission volume by deploying artificial intelligence(AI)models that provide abilities of semantic extraction and recovery.Nevertheless,it is not feasible to preload all AI models on resource-constrained terminals.Thus,in-time model transmission becomes a crucial problem.This paper proposes an intellicise model transmission architecture to guarantee the reliable transmission of models for semantic communication.The mathematical relationship between model size and performance is formulated by employing a recognition error function supported with experimental data.We consider the characteristics of wireless channels and derive the closed-form expression of model transmission outage probability(MTOP)over the Rayleigh channel.Besides,we define the effective model accuracy(EMA)to evaluate the model transmission performance of both communication and intelligence.Then we propose a joint model selection and resource allocation(JMSRA)algorithm to maximize the average EMA of all users.Simulation results demonstrate that the average EMA of the JMSRA algorithm outperforms baseline algorithms by about 22%.
基金supported by the National Natural Science Foundation of China(No.61971062)BUPT Excellent Ph.D.Students Foundation(CX2022153)。
文摘Video transmission requires considerable bandwidth,and current widely employed schemes prove inadequate when confronted with scenes featuring prominently.Motivated by the strides in talkinghead generative technology,the paper introduces a semantic transmission system tailored for talking-head videos.The system captures semantic information from talking-head video and faithfully reconstructs source video at the receiver,only one-shot reference frame and compact semantic features are required for the entire transmission.Specifically,we analyze video semantics in the pixel domain frame-by-frame and jointly process multi-frame semantic information to seamlessly incorporate spatial and temporal information.Variational modeling is utilized to evaluate the diversity of importance among group semantics,thereby guiding bandwidth resource allocation for semantics to enhance system efficiency.The whole endto-end system is modeled as an optimization problem and equivalent to acquiring optimal rate-distortion performance.We evaluate our system on both reference frame and video transmission,experimental results demonstrate that our system can improve the efficiency and robustness of communications.Compared to the classical approaches,our system can save over 90%of bandwidth when user perception is close.
基金supported by the National Natural Science Foundation of China(No.62293481,No.62071058)。
文摘As a novel paradigm,semantic communication provides an effective solution for breaking through the future development dilemma of classical communication systems.However,it remains an unsolved problem of how to measure the information transmission capability for a given semantic communication method and subsequently compare it with the classical communication method.In this paper,we first present a review of the semantic communication system,including its system model and the two typical coding and transmission methods for its implementations.To address the unsolved issue of the information transmission capability measure for semantic communication methods,we propose a new universal performance measure called Information Conductivity.We provide the definition and the physical significance to state its effectiveness in representing the information transmission capabilities of the semantic communication systems and present elaborations including its measure methods,degrees of freedom,and progressive analysis.Experimental results in image transmission scenarios validate its practical applicability.
基金supported by a research grant from Seoul Women’s University(2020)financially supported by Hansung University
文摘Purpose:This study attempts to propose an abstract model by gathering concepts that can focus on resource representation and description in a digital curation model and suggest a conceptual model that emphasizes semantic enrichment in a digital curation model.Design/methodology/approach:This study conducts a literature review to analyze the preceding curation models,DCC CLM,DCC&U,UC3,and DCN.Findings:The concept of semantic enrichment is expressed in a single word,SEMANTIC in this study.The Semantic Enrichment Model,SEMANTIC has elements,subject,extraction,multi-language,authority,network,thing,identity,and connect.Research limitations:This study does not reflect the actual information environment because it focuses on the concepts of the representation of digital objects.Practical implications:This study presents the main considerations for creating and reinforcing the description and representation of digital objects when building and developing digital curation models in specific institutions.Originality/value:This study summarizes the elements that should be emphasized in the representation of digital objects in terms of information organization.
基金Supported by the Science and Technology Innovation Plan of Beijing Institute of Technology(2013)
文摘A new method is proposed for constructing the Chinese sentential semantic structure in this paper. The method adopts the features including predicates, relations between predicates and basic arguments, relations between words, and case types to train the models of CRF ＋ ＋ and de- pendency parser. On the basis of the data set in Beijing Forest Studio-Chinese Tagged Corpus （ BFS- CTC）, the proposed method obtains precision value of 73.63% in open test. This result shows that the formalized computer processing can construct the sentential semantic structure absolutely. The features of predicates, topic and comment extracted with the method can be applied in Chinese in- formation processing directly for promoting the development of Chinese semantic analysis. The method makes the analysis of sentential semantic analysis based on large scale of data possible. It is a tool for expanding the corpus and has certain theoretical research and practical application value.
基金supported by the National Key Research and Development Program(No.2016YFB0800302)
文摘With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.
基金supported in part by the National Science and technology support program of China No. 2014BAH29F05the National High-Tech R&D Program (863 Program) No. 2015AA01A705+3 种基金the National Natural Science Foundation of China under Grant No. 61572072the National Science and Technology Major Project No. 2015ZX03001041the Fundamental Research Funds for the Central Universities No. FRF-TP-14-046A2"Research on the System of Personalized Education using Big Data"
文摘Cloud storage has the characteristics of distributed and virtual, and it makes the ownership rights and management rights of users data separated. The master-slave architecture of cloud storage has a problem of single point failure. In this paper, we provide a cloud storage architecture model based on Semantic equivalence. According to semantic matching degree, this architecture divides the nodes into node cluster by creating semantic tree and maintains system routing through semantic hypergraph. Through simulation experiments show that dividing network into semantic can enhance scalability and flexibility of the system, and it can improve the efficiency of network organization and the security of cloud storage system, at the same time, it can also reduce the cloud data storage and the delay of reading time.
基金supported by Electric energy data mining and intelligent analysis technology research and application projects of Shenzhen Power Supply Bureau, Ltd
文摘Wireless smart home system is to facilitate people's lives and it trend to adopt a more intelligent way to provide services. It is very desirable in the recent SH market for the system to recognize users' behaviors and automatically response the corresponding activities to satisfy users' actual demands. However, activity models in the existing approaches are usually defined separately through knowledge-driven methods. These approaches cause that the activity models can't be matched with the services dynamically. To address the problem, we develop the semantic association model and a novel approach of activity recognition and guidance is presented. In our approach, the smart devices and users' requirements are described by semantic models. When the requirements are detected and understood, smart gateway can provide appropriate services, achieving activity assistance. The semantic association model allows all related elements in smart home connect with each other logically. The approach has been implemented and the results show that the success rate of the approach based on semantic association model is higher than 33% at average as compared to the approach based on predefined models. The proposed approach can effectively help people who are in trouble with learning or remembering in the common life.
基金This work was supported by the national key research development plan(Project No.YS2018YFB1403703)research project of the communication university of china(Project No.CUC200D058).
文摘Learning-based multi-task models have been widely used in various scene understanding tasks,and complement each other,i.e.,they allow us to consider prior semantic information to better infer depth.We boost the unsupervised monocular depth estimation using semantic segmentation as an auxiliary task.To address the lack of cross-domain datasets and catastrophic forgetting problems encountered in multi-task training,we utilize existing methodology to obtain redundant segmentation maps to build our cross-domain dataset,which not only provides a new way to conduct multi-task training,but also helps us to evaluate results compared with those of other algorithms.In addition,in order to comprehensively use the extracted features of the two tasks in the early perception stage,we use a strategy of sharing weights in the network to fuse cross-domain features,and introduce a novel multi-task loss function to further smooth the depth values.Extensive experiments on KITTI and Cityscapes datasets show that our method has achieved state-of-the-art performance in the depth estimation task,as well improved semantic segmentation.
文摘随着移动通信技术的发展演进,6G(6th-Generation)网络作为新一代智能化数字信息基础设施,将不再仅聚焦信号的传输和复现,更需要基于电磁传播过程实现对周围环境的高效感知和理解,从而获取信道语义知识,协助智能通信体的预测、决策、波束成形等.因此,相较于传统信道而言,赋予无线信道模型对物理环境的语义理解、重构、表达能力,已成为智能无线信道模型的重要特征.本文提出了一种无线信道语义的分析和建模方法,将信道语义定义为状态语义、行为语义和事件语义3种层级,分别对应信道瞬态多径、信道时变轨迹和信道拓扑结构.此外,基于车载通感一体化(Integrated Sensing And Communication,ISAC)信道测量系统,开展了28 GHz下面向信道语义表征的无线信道测量,基于实测数据对信道语义进行解构、标识、建模,重点分析了3种不同语义下的信道多径分布特性,完成了语义导向的信道生成,结果表明信道语义模型能够在生成较准确信道的同时,表达更丰富的语义信息.本文工作是在语义层面上探索智能信道建模的新方法,通过深入挖掘无线信道的内在语义特征,促进通信系统在理解和认知环境方面的能力,从而提高通信效率和质量.
