Arabic Sign Language (ArSL) is the native language for the Arab deaf community. ArSL allows deaf people to communicate among themselves and with non-deaf people around them to express their needs, thoughts and feeling...Arabic Sign Language (ArSL) is the native language for the Arab deaf community. ArSL allows deaf people to communicate among themselves and with non-deaf people around them to express their needs, thoughts and feelings. Opposite to spoken languages, Sign Language (SL) depends on hands and facial expression to express the thought instead of sounds. In recent years, interest in translating sign language automatically for different languages has increased. However, a small set of these works are specialized in ArSL. Basically, these works translate word by word without taking care of the semantics of the translated sentence or the translation rules of Arabic text to Arabic sign language. In this paper we present a proposed system for semantically translating Arabic text to Arabic SignWriting in the jurisprudence of prayer domain. The system is designed to translate Arabic text by applying Arabic Sign Language (ArSL) grammatical rules as well as semantically looking up the words in domain ontology. The results of qualitatively evaluating the system based on a SignWriting expert judgment proved the correctness of the translation results.展开更多
人类本身奥妙的生物机能常常为新技术的开发带来启示,探寻并模拟人类生物机理有助于我们提高对智能生命本质的最终认识。生物启发智能意味着,我们可以参考生物的一些功能,并将其应用到智能计算机系统中。semantic system ag公司研发出...人类本身奥妙的生物机能常常为新技术的开发带来启示,探寻并模拟人类生物机理有助于我们提高对智能生命本质的最终认识。生物启发智能意味着,我们可以参考生物的一些功能,并将其应用到智能计算机系统中。semantic system ag公司研发出新一代的计算机处理器,通过模拟大脑处理和储存信息的机理,以及破解神经代码,使计算机可以像人脑一样思考。这是人类第一次能够利用计算机芯片进行复杂思维和过程分析,并且得到与人类思考相同的结果。展开更多
Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and stru...Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.展开更多
In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic q...In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic queries.Additionally,they typically rely on honest but curious cloud servers,which introduces the risk of repudiation.Furthermore,the combined operations of search and verification increase system load,thereby reducing performance.Traditional verification mechanisms,which rely on complex hash constructions,suffer from low verification efficiency.To address these challenges,this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification.Building on existing single and multi-keyword search methods,the scheme uses vector models to semantically train the dataset,enabling it to retain semantic information and achieve context-aware encrypted retrieval,significantly improving search accuracy.Additionally,a blockchain-based updatable master-slave chain storage model is designed,where the master chain stores encrypted keyword indexes and the slave chain stores verification information generated by zero-knowledge proofs,thus balancing system load while improving search and verification efficiency.Finally,an improved non-interactive zero-knowledge proof mechanism is introduced,reducing the computational complexity of verification and ensuring efficient validation of search results.Experimental results demonstrate that the proposed scheme offers stronger security,balanced overhead,and higher search verification efficiency.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
In consideration of the limitation of super-peer overlay network, the semantic information was introduced into the super-peers＇ organization. A novel P2P （peer-to-peer） searching model, SSP2P, was put forward. The ...In consideration of the limitation of super-peer overlay network, the semantic information was introduced into the super-peers＇ organization. A novel P2P （peer-to-peer） searching model, SSP2P, was put forward. The peers in the model were organized in a natural area autonomy system （AAS） based on the smallworld theory. A super-peer was selected in each AAS based on power law; and all the super-peers formed different super-peer semantic networks. Thus, a hierarchical super-peer overlay network was formed. The results show that the model reduces the communication cost and enhances the search efficiency while ensuring the system expansibility. It proves that the introduction of semantic information in the construction of a super-peer overlay is favorable to P2P system capability.展开更多
In order to fully realize semantic interoperability among distributed and heterogeneous applications on the web, a set of effective interoperability mechanisms is presented. This mechanism adopts service interactive i...In order to fully realize semantic interoperability among distributed and heterogeneous applications on the web, a set of effective interoperability mechanisms is presented. This mechanism adopts service interactive interfaces （SII） and service aggregative interfaces （SAI） modeled with abstract state machine （ASM） to abstractly describe the behavior of the invoked web service instances, which makes business processing accurately specify tasks and effectively solves the problems of communication and collaboration between service providers and service requesters. The mechanism also uses appropriate mediators to solve the problems of information and coinmunication incompatibility during the course of service interaction, which is convenient for service interoperability, sharing and integration. The mechanism＇ s working principle and interoperability implementation are illustrated by a use case in detail.展开更多
To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of to...To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of tools for supporting the sharing of knowledge and promoting NM collaboration. A 5-tuple based semantic information retrieval model is proposed, which includes the interoperation on the semantic layer, and a test process is given for this model. The recall ratio and the precision ratio of manufacturing knowledge retrieval are proved to be greatly improved by evaluation. Thus, a practical and reliable approach based on the semantic web is provided for solving the correlated concrete problems in regional networked manufacturing.展开更多
In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve ...In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and seman- tic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.展开更多
With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image t...With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image transmission as an example, from the semantic communication's view, not all pixels in the images are equally important for certain receivers. The existing semantic communication systems directly perform semantic encoding and decoding on the whole image, in which the region of interest cannot be identified. In this paper, we propose a novel semantic communication system for image transmission that can distinguish between Regions Of Interest (ROI) and Regions Of Non-Interest (RONI) based on semantic segmentation, where a semantic segmentation algorithm is used to classify each pixel of the image and distinguish ROI and RONI. The system also enables high-quality transmission of ROI with lower communication overheads by transmissions through different semantic communication networks with different bandwidth requirements. An improved metric θPSNR is proposed to evaluate the transmission accuracy of the novel semantic transmission network. Experimental results show that our proposed system achieves a significant performance improvement compared with existing approaches, namely, existing semantic communication approaches and the conventional approach without semantics.展开更多
A reputation mechanism is introduced in P2P- based Semantic Web to solve the problem of lacking trust. It enables Semantic Web to utilize reputation information based on semantic similarity of peers in the network. Th...A reputation mechanism is introduced in P2P- based Semantic Web to solve the problem of lacking trust. It enables Semantic Web to utilize reputation information based on semantic similarity of peers in the network. This approach is evaluated in a simulation of a content sharing system and the experiments show that the system with reputation mechanism outperforms the system without it.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
Cyber physical system(CPS)provides more powerful service by cyber and physical features through the wireless communication.As a kind of social organized network system,a fundamental question of CPS is to achieve servi...Cyber physical system(CPS)provides more powerful service by cyber and physical features through the wireless communication.As a kind of social organized network system,a fundamental question of CPS is to achieve service self-organization with its nodes autonomously working in both physical and cyber environments.To solve the problem,the social nature of nodes in CPS is firstly addressed,and then a formal social semantic descriptions is presented for physical environment,node service and task in order to make the nodes communicate automatically and physical environment sensibly.Further,the Horn clause is introduced to represent the reasoning rules of service organizing.Based on the match function,which is defined for measurement between semantics,the semantic aware measurement is presented to evaluate whether environment around a node can satisfy the task requirement or not.Moreover,the service capacity evaluation method for nodes is addressed to find out the competent service from both cyber and physical features of nodes.According to aforementioned two measurements,the task semantic decomposition algorithm and the organizing matrix are defined and the service self-organizing mechanism for CPS is proposed.Finally,examinations are given to further verify the efficiency and feasibility of the proposed mechanism.展开更多
Discrete event system(DES)models promote system engineering,including system design,verification,and assessment.The advancement in manufacturing technology has endowed us to fabricate complex industrial systems.Conseq...Discrete event system(DES)models promote system engineering,including system design,verification,and assessment.The advancement in manufacturing technology has endowed us to fabricate complex industrial systems.Consequently,the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative.Moreover,industrial systems are no longer quiescent,thus the intelligent operations of the systems should be dynamically specified in the model.In this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the model.In traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-consuming and error-prone process.Compared with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continuously adding a new semantic.Thus,this general modeling approach has wide potential for future complex and intelligent industrial systems.展开更多
Road marking detection is an important branch in autonomous driving,understanding the road information.In recent years,deep-learning-based semantic segmentation methods for road marking detection have been arising sin...Road marking detection is an important branch in autonomous driving,understanding the road information.In recent years,deep-learning-based semantic segmentation methods for road marking detection have been arising since they can generalize detection result well under complicated environments and hold rich pixel-level semantic information.Nevertheless,the previous methods mostly study the training process of the segmentation network,while omitting the time cost of manually annotating pixel-level data.Besides,the pixel-level semantic segmentation results need to be fitted into more reliable and compact models so that geometrical information of road markings can be explicitly obtained.In order to tackle the above problems,this paper describes a semantic segmentation-based road marking detection method using around view monitoring system.A semiautomatic semantic annotation platform is developed,which exploits an auxiliary segmentation graph to speed up the annotation process while guaranteeing the annotation accuracy.A segmentation-based detection module is also described,which models the semantic segmentation results for the more robust and compact analysis.The proposed detection module is composed of three parts:vote-based segmentation fusion filtering,graph-based road marking clustering,and road-marking fitting.Experiments under various scenarios show that the semantic segmentation-based detection method can achieve accurate,robust,and real-time detection performance.展开更多
CAD model retrieval based on functional semantics is more significant than content-based 3D model retrieval during the mechanical conceptual design phase. However, relevant research is still not fully discussed. There...CAD model retrieval based on functional semantics is more significant than content-based 3D model retrieval during the mechanical conceptual design phase. However, relevant research is still not fully discussed. Therefore, a functional semantic-based CAD model annotation and retrieval method is proposed to support mechanical conceptual design and design reuse, inspire designer creativity through existing CAD models, shorten design cycle, and reduce costs. Firstly, the CAD model functional semantic ontology is constructed to formally represent the functional semantics of CAD models and describe the mechanical conceptual design space comprehensively and consistently. Secondly, an approach to represent CAD models as attributed adjacency graphs（AAG） is proposed. In this method, the geometry and topology data are extracted from STEP models. On the basis of AAG, the functional semantics of CAD models are annotated semi-automatically by matching CAD models that contain the partial features of which functional semantics have been annotated manually, thereby constructing CAD Model Repository that supports model retrieval based on functional semantics. Thirdly, a CAD model retrieval algorithm that supports multi-function extended retrieval is proposed to explore more potential creative design knowledge in the semantic level. Finally, a prototype system, called Functional Semantic-based CAD Model Annotation and Retrieval System（FSMARS）, is implemented. A case demonstrates that FSMARS can successfully botain multiple potential CAD models that conform to the desired function. The proposed research addresses actual needs and presents a new way to acquire CAD models in the mechanical conceptual design phase.展开更多
Labelled transition systems(LTSs) are widely used to formally describe system behaviour.The labels of LTS are extended to offer a more satisfactory description of behaviour by refining the abstract labels into multiva...Labelled transition systems(LTSs) are widely used to formally describe system behaviour.The labels of LTS are extended to offer a more satisfactory description of behaviour by refining the abstract labels into multivariate polynomials.These labels can be simplified by numerous numerical approximation methods.Those LTSs that can not apply failures semantics equivalence in description and verification may have a chance after using approximation on labels.The technique that combines approximation and failures semantics equivalence effectively alleviates the computational complexity and minimizes LTS.展开更多
Software industry has evolved to multi-product and multi-platform development based on a mix of proprietary and open source components. Such integration has occurred in software ecosystems through a software product l...Software industry has evolved to multi-product and multi-platform development based on a mix of proprietary and open source components. Such integration has occurred in software ecosystems through a software product line engineering (SPLE) process. However, metadata are underused in the SPLE and interoperability challenge. The proposed method is first, a semantic metadata enrichment software ecosystem (SMESE) to support multi-platform metadata driven applications, and second, based on mapping ontologies SMESE aggregates and enriches metadata to create a semantic master metadata catalogue (SMMC). The proposed SPLE process uses a component-based software development approach for integrating distributed content management enterprise applications, such as digital libraries. To perform interoperability between existing metadata models (such as Dublin Core, UNIMARC, MARC21, RDF/RDA and BIBFRAME), SMESE implements an ontology mapping model. SMESE consists of nine sub-systems: 1) Metadata initiatives & concordance rules;2) Harvesting of web metadata & data;3) Harvesting of authority metadata & data;4) Rule-based semantic metadata external enrichment;5) Rule-based semantic metadata internal enrichment;6) Semantic metadata external & internal enrichment synchronization;7) User interest-based gateway;8) Semantic master catalogue. To conclude, this paper proposes a decision support process, called SPLE decision support process (SPLE-DSP) which is then used by SMESE to support dynamic reconfiguration. SPLE-DSP consists of a dynamic and optimized metadata-based reconfiguration model. SPLE-DSP takes into account runtime metadata-based variability functionalities, context-awareness and self-adaptation. It also presents the design and implementation of a working prototype of SMESE applied to a semantic digital library.展开更多
The terminology of "fuzzy logic" has been formed and employed with different meanings bya large number of researchers soon after Zadeh published his initiate paper (see [1]), butperhaps the most serious work...The terminology of "fuzzy logic" has been formed and employed with different meanings bya large number of researchers soon after Zadeh published his initiate paper (see [1]), butperhaps the most serious work on fuzzy logic is the Pavelka’s series papers (see [2]) whichprovided a systematical theory of fuzzy logic mathematically and profoundly, and hence展开更多
Semantic Internet of Things is an open-world service ecosystem formed on the basis of the Semantic Web, Internet of Things, and social networks. It forms and integrates physical space, cyberspace,and social space. The...Semantic Internet of Things is an open-world service ecosystem formed on the basis of the Semantic Web, Internet of Things, and social networks. It forms and integrates physical space, cyberspace,and social space. The data friction problem is the key issue of the semantic Internet from theory to practice. In order to solve the problem of information interoperation and data friction,it is necessary to deal with the heterogeneity, dynamicity, and uncertainty of data in the semantic Internet of Things. For this reason, this paper constructs a semantic object networking context aware system based on dynamic Bayesian network. The overall architecture of context awareness system based on semantic Internet of Things is proposed. The hierarchical structure of the framework and the functions implemented by each layer are introduced in detail. The contextual awareness system based on semantic Internet of Things is introduced into the overall design and implementation. The design and implementation process of each layer structure of the system is described in detail.展开更多
文摘Arabic Sign Language (ArSL) is the native language for the Arab deaf community. ArSL allows deaf people to communicate among themselves and with non-deaf people around them to express their needs, thoughts and feelings. Opposite to spoken languages, Sign Language (SL) depends on hands and facial expression to express the thought instead of sounds. In recent years, interest in translating sign language automatically for different languages has increased. However, a small set of these works are specialized in ArSL. Basically, these works translate word by word without taking care of the semantics of the translated sentence or the translation rules of Arabic text to Arabic sign language. In this paper we present a proposed system for semantically translating Arabic text to Arabic SignWriting in the jurisprudence of prayer domain. The system is designed to translate Arabic text by applying Arabic Sign Language (ArSL) grammatical rules as well as semantically looking up the words in domain ontology. The results of qualitatively evaluating the system based on a SignWriting expert judgment proved the correctness of the translation results.
文摘人类本身奥妙的生物机能常常为新技术的开发带来启示,探寻并模拟人类生物机理有助于我们提高对智能生命本质的最终认识。生物启发智能意味着,我们可以参考生物的一些功能,并将其应用到智能计算机系统中。semantic system ag公司研发出新一代的计算机处理器,通过模拟大脑处理和储存信息的机理,以及破解神经代码,使计算机可以像人脑一样思考。这是人类第一次能够利用计算机芯片进行复杂思维和过程分析,并且得到与人类思考相同的结果。
文摘Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.
基金supported in part by the National Natural Science Foundation of China under Grant 62262073in part by the Yunnan Provincial Ten Thousand People Program for Young Top Talents under Grant YNWR-QNBJ-2019-237in part by the Yunnan Provincial Major Science and Technology Special Program under Grant 202402AD080002.
文摘In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic queries.Additionally,they typically rely on honest but curious cloud servers,which introduces the risk of repudiation.Furthermore,the combined operations of search and verification increase system load,thereby reducing performance.Traditional verification mechanisms,which rely on complex hash constructions,suffer from low verification efficiency.To address these challenges,this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification.Building on existing single and multi-keyword search methods,the scheme uses vector models to semantically train the dataset,enabling it to retain semantic information and achieve context-aware encrypted retrieval,significantly improving search accuracy.Additionally,a blockchain-based updatable master-slave chain storage model is designed,where the master chain stores encrypted keyword indexes and the slave chain stores verification information generated by zero-knowledge proofs,thus balancing system load while improving search and verification efficiency.Finally,an improved non-interactive zero-knowledge proof mechanism is introduced,reducing the computational complexity of verification and ensuring efficient validation of search results.Experimental results demonstrate that the proposed scheme offers stronger security,balanced overhead,and higher search verification efficiency.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金The National Natural Science Foundation of China(No.60573127), Specialized Research Fund for the Doctoral Program of Higher Education (No.20040533036).
文摘In consideration of the limitation of super-peer overlay network, the semantic information was introduced into the super-peers＇ organization. A novel P2P （peer-to-peer） searching model, SSP2P, was put forward. The peers in the model were organized in a natural area autonomy system （AAS） based on the smallworld theory. A super-peer was selected in each AAS based on power law; and all the super-peers formed different super-peer semantic networks. Thus, a hierarchical super-peer overlay network was formed. The results show that the model reduces the communication cost and enhances the search efficiency while ensuring the system expansibility. It proves that the introduction of semantic information in the construction of a super-peer overlay is favorable to P2P system capability.
基金The Natural Science Foundation of Hunan Province (No.05JJ30122),the Education Department Foundation of Hunan Prov-ince (No.05C519).
文摘In order to fully realize semantic interoperability among distributed and heterogeneous applications on the web, a set of effective interoperability mechanisms is presented. This mechanism adopts service interactive interfaces （SII） and service aggregative interfaces （SAI） modeled with abstract state machine （ASM） to abstractly describe the behavior of the invoked web service instances, which makes business processing accurately specify tasks and effectively solves the problems of communication and collaboration between service providers and service requesters. The mechanism also uses appropriate mediators to solve the problems of information and coinmunication incompatibility during the course of service interaction, which is convenient for service interoperability, sharing and integration. The mechanism＇ s working principle and interoperability implementation are illustrated by a use case in detail.
基金The National High Technology Research and Devel-opment Program of China (863Program) (No2003AA1Z2560,2002AA414060)the Key Science and Technology Program of Shaanxi Province (No2006K04-G10)
文摘To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of tools for supporting the sharing of knowledge and promoting NM collaboration. A 5-tuple based semantic information retrieval model is proposed, which includes the interoperation on the semantic layer, and a test process is given for this model. The recall ratio and the precision ratio of manufacturing knowledge retrieval are proved to be greatly improved by evaluation. Thus, a practical and reliable approach based on the semantic web is provided for solving the correlated concrete problems in regional networked manufacturing.
基金the Specialized Research Program Fundthe Doctoral Program of Higher Education of China (20050007023)the Natural Science Foundation of Shandong Province(Y2004G04)
文摘In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and seman- tic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.
基金supported in part by collaborative research with Toyota Motor Corporation,in part by ROIS NII Open Collaborative Research under Grant 21S0601,in part by JSPS KAKENHI under Grants 20H00592,21H03424.
文摘With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image transmission as an example, from the semantic communication's view, not all pixels in the images are equally important for certain receivers. The existing semantic communication systems directly perform semantic encoding and decoding on the whole image, in which the region of interest cannot be identified. In this paper, we propose a novel semantic communication system for image transmission that can distinguish between Regions Of Interest (ROI) and Regions Of Non-Interest (RONI) based on semantic segmentation, where a semantic segmentation algorithm is used to classify each pixel of the image and distinguish ROI and RONI. The system also enables high-quality transmission of ROI with lower communication overheads by transmissions through different semantic communication networks with different bandwidth requirements. An improved metric θPSNR is proposed to evaluate the transmission accuracy of the novel semantic transmission network. Experimental results show that our proposed system achieves a significant performance improvement compared with existing approaches, namely, existing semantic communication approaches and the conventional approach without semantics.
基金Supported by the National Natural Science Foun-dation of China (60173026) the Ministry of Education Key Project(105071) Foundation of E-Institute of Shanghai HighInstitutions(200301)
文摘A reputation mechanism is introduced in P2P- based Semantic Web to solve the problem of lacking trust. It enables Semantic Web to utilize reputation information based on semantic similarity of peers in the network. This approach is evaluated in a simulation of a content sharing system and the experiments show that the system with reputation mechanism outperforms the system without it.
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(61103069,71171148)the National High-Tech Research and Development Plan of China(″863″ Plan)(2012BAD35B01)+2 种基金the Innovation Program of Shanghai Municipal Education Commission(13YZ052)the Shanghai Committee of Science and Technology(11DZ1501703,11dz12106001)the Program of Shanghai Normal University(DXL125,DCL201302)
文摘Cyber physical system(CPS)provides more powerful service by cyber and physical features through the wireless communication.As a kind of social organized network system,a fundamental question of CPS is to achieve service self-organization with its nodes autonomously working in both physical and cyber environments.To solve the problem,the social nature of nodes in CPS is firstly addressed,and then a formal social semantic descriptions is presented for physical environment,node service and task in order to make the nodes communicate automatically and physical environment sensibly.Further,the Horn clause is introduced to represent the reasoning rules of service organizing.Based on the match function,which is defined for measurement between semantics,the semantic aware measurement is presented to evaluate whether environment around a node can satisfy the task requirement or not.Moreover,the service capacity evaluation method for nodes is addressed to find out the competent service from both cyber and physical features of nodes.According to aforementioned two measurements,the task semantic decomposition algorithm and the organizing matrix are defined and the service self-organizing mechanism for CPS is proposed.Finally,examinations are given to further verify the efficiency and feasibility of the proposed mechanism.
基金supported by the National Natural Science Foundation of China(U21B2074,52105070).
文摘Discrete event system(DES)models promote system engineering,including system design,verification,and assessment.The advancement in manufacturing technology has endowed us to fabricate complex industrial systems.Consequently,the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative.Moreover,industrial systems are no longer quiescent,thus the intelligent operations of the systems should be dynamically specified in the model.In this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the model.In traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-consuming and error-prone process.Compared with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continuously adding a new semantic.Thus,this general modeling approach has wide potential for future complex and intelligent industrial systems.
基金the National Natural Science Foundation of China(Nos.U1764264 and 61873165)the Shanghai Automotive Industry Science and Technology Development Foundation(No.1807)。
文摘Road marking detection is an important branch in autonomous driving,understanding the road information.In recent years,deep-learning-based semantic segmentation methods for road marking detection have been arising since they can generalize detection result well under complicated environments and hold rich pixel-level semantic information.Nevertheless,the previous methods mostly study the training process of the segmentation network,while omitting the time cost of manually annotating pixel-level data.Besides,the pixel-level semantic segmentation results need to be fitted into more reliable and compact models so that geometrical information of road markings can be explicitly obtained.In order to tackle the above problems,this paper describes a semantic segmentation-based road marking detection method using around view monitoring system.A semiautomatic semantic annotation platform is developed,which exploits an auxiliary segmentation graph to speed up the annotation process while guaranteeing the annotation accuracy.A segmentation-based detection module is also described,which models the semantic segmentation results for the more robust and compact analysis.The proposed detection module is composed of three parts:vote-based segmentation fusion filtering,graph-based road marking clustering,and road-marking fitting.Experiments under various scenarios show that the semantic segmentation-based detection method can achieve accurate,robust,and real-time detection performance.
基金Supported by National Natural Science Foundation of China (Grant No.51175287)National Science and Technology Major Project of China (Grant No.2011ZX02403)
文摘CAD model retrieval based on functional semantics is more significant than content-based 3D model retrieval during the mechanical conceptual design phase. However, relevant research is still not fully discussed. Therefore, a functional semantic-based CAD model annotation and retrieval method is proposed to support mechanical conceptual design and design reuse, inspire designer creativity through existing CAD models, shorten design cycle, and reduce costs. Firstly, the CAD model functional semantic ontology is constructed to formally represent the functional semantics of CAD models and describe the mechanical conceptual design space comprehensively and consistently. Secondly, an approach to represent CAD models as attributed adjacency graphs（AAG） is proposed. In this method, the geometry and topology data are extracted from STEP models. On the basis of AAG, the functional semantics of CAD models are annotated semi-automatically by matching CAD models that contain the partial features of which functional semantics have been annotated manually, thereby constructing CAD Model Repository that supports model retrieval based on functional semantics. Thirdly, a CAD model retrieval algorithm that supports multi-function extended retrieval is proposed to explore more potential creative design knowledge in the semantic level. Finally, a prototype system, called Functional Semantic-based CAD Model Annotation and Retrieval System（FSMARS）, is implemented. A case demonstrates that FSMARS can successfully botain multiple potential CAD models that conform to the desired function. The proposed research addresses actual needs and presents a new way to acquire CAD models in the mechanical conceptual design phase.
基金National Natural Science Foundation of China(No.11371003)Natural Science Foundations of Guangxi,China(No.2011GXNSFA018154,No.2012GXNSFGA060003)+2 种基金Science and Technology Foundation of Guangxi,China(No.10169-1)Scientific Research Project from Guangxi Education Department,China(No.201012MS274)Open Research Fund Program of Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis,China(No.HCIC201301)
文摘Labelled transition systems(LTSs) are widely used to formally describe system behaviour.The labels of LTS are extended to offer a more satisfactory description of behaviour by refining the abstract labels into multivariate polynomials.These labels can be simplified by numerous numerical approximation methods.Those LTSs that can not apply failures semantics equivalence in description and verification may have a chance after using approximation on labels.The technique that combines approximation and failures semantics equivalence effectively alleviates the computational complexity and minimizes LTS.
文摘Software industry has evolved to multi-product and multi-platform development based on a mix of proprietary and open source components. Such integration has occurred in software ecosystems through a software product line engineering (SPLE) process. However, metadata are underused in the SPLE and interoperability challenge. The proposed method is first, a semantic metadata enrichment software ecosystem (SMESE) to support multi-platform metadata driven applications, and second, based on mapping ontologies SMESE aggregates and enriches metadata to create a semantic master metadata catalogue (SMMC). The proposed SPLE process uses a component-based software development approach for integrating distributed content management enterprise applications, such as digital libraries. To perform interoperability between existing metadata models (such as Dublin Core, UNIMARC, MARC21, RDF/RDA and BIBFRAME), SMESE implements an ontology mapping model. SMESE consists of nine sub-systems: 1) Metadata initiatives & concordance rules;2) Harvesting of web metadata & data;3) Harvesting of authority metadata & data;4) Rule-based semantic metadata external enrichment;5) Rule-based semantic metadata internal enrichment;6) Semantic metadata external & internal enrichment synchronization;7) User interest-based gateway;8) Semantic master catalogue. To conclude, this paper proposes a decision support process, called SPLE decision support process (SPLE-DSP) which is then used by SMESE to support dynamic reconfiguration. SPLE-DSP consists of a dynamic and optimized metadata-based reconfiguration model. SPLE-DSP takes into account runtime metadata-based variability functionalities, context-awareness and self-adaptation. It also presents the design and implementation of a working prototype of SMESE applied to a semantic digital library.
文摘The terminology of "fuzzy logic" has been formed and employed with different meanings bya large number of researchers soon after Zadeh published his initiate paper (see [1]), butperhaps the most serious work on fuzzy logic is the Pavelka’s series papers (see [2]) whichprovided a systematical theory of fuzzy logic mathematically and profoundly, and hence
文摘Semantic Internet of Things is an open-world service ecosystem formed on the basis of the Semantic Web, Internet of Things, and social networks. It forms and integrates physical space, cyberspace,and social space. The data friction problem is the key issue of the semantic Internet from theory to practice. In order to solve the problem of information interoperation and data friction,it is necessary to deal with the heterogeneity, dynamicity, and uncertainty of data in the semantic Internet of Things. For this reason, this paper constructs a semantic object networking context aware system based on dynamic Bayesian network. The overall architecture of context awareness system based on semantic Internet of Things is proposed. The hierarchical structure of the framework and the functions implemented by each layer are introduced in detail. The contextual awareness system based on semantic Internet of Things is introduced into the overall design and implementation. The design and implementation process of each layer structure of the system is described in detail.
