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A highly reliable encoding and decoding communication framework based on semantic information

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Digital Communications and Networks 2024年第3期10卷 509-518页

作者： Yichi Zhang Haitao Zhao Kuo Cao Li Zhou Zhe Wang Yueling Liu Jibo Wei College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China

Increasing research has focused on semantic communication,the goal of which is to convey accurately the meaning instead of transmitting symbols from the sender to the *** this paper,we design a novel encoding and decoding semantic communication framework,which adopts the semantic information and the contextual correlations between items to optimize the performance of a communication system over various *** the sender side,the average semantic loss caused by the wrong detection is defined,and a semantic source encoding strategy is developed to minimize the average semantic *** further improve communication reliability,a decoding strategy that utilizes the semantic and the context information to recover messages is proposed in the *** simulation results validate the superior performance of our strategies over state-of-the-art semantic coding and decoding policies on different communication channels.

关键词： semantic information semantic encoding method Context-based decoding method

A Theory of semantic information(Invited Paper)

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China Communications 2017年第1期14卷 1-17页

作者： Yixin Zhong Beijing University of Posts and Telecommunications Haidian District Beijing 100876 China

The information really useful to humans must be the trinity of its three components: the form termed syntactic information, the meaning termed semantic information, and the utility termed pragmatic information. But the theory of information set up by Shannon in 1948 is a statistical theory of syntactic information. Thus, the trinity of information theories needs to be established as urgently as possible. Such a theory of semantic information will be presented in the paper and it will also be proved that it is the semantic information that is the unique representative of the trinity. This is why the title of the paper is set to "a theory of semantic information" without mentioning the pragmatic information.

关键词： semantic information information ecology knowledge intelligence information-knowledge-intelligence conversion

A Text Sentiment Classification Modeling Method Based on Coordinated CNN-LSTM-Attention Model

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Chinese Journal of Electronics 2019年第1期28卷 120-126页

作者： ZHANG Yangsen ZHENG Jia JIANG Yuru HUANG Gaijuan CHEN Ruoyu Institute of Intelligent information Processing Beijing Information Science and Technology University Beijing Laboratory of National Economic Security Early-Warning Engineering

The major challenge that text sentiment classification modeling faces is how to capture the intrinsic semantic, emotional dependence information and the key part of the emotional expression of text. To solve this problem, we proposed a Coordinated CNNLSTM-Attention(CCLA) model. We learned the vector representations of sentence with CCLA unit. semantic and emotional information of sentences and their relations are adaptively encoded to vector representations of *** used softmax regression classifier to identify the sentiment tendencies in the text. Compared with other methods, the CCLA model can well capture the local and long distance semantic and emotional *** results demonstrated the effectiveness of CCLA model. It shows superior performances over several state-of-the-art baseline methods.

关键词： Coordinated CNN-LSTM-Attention Sentiment analysis Text modeling semantic information

Toward Wisdom-Evolutionary and Primitive-Concise 6G:A New Paradigm of semantic Communication Networks

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Engineering 2022年第1期8卷 60-73页

作者： Ping Zhang Wenjun Xu Hui Gao Kai Niu Xiaodong Xu Xiaoqi Qin Caixia Yuan Zhijin Qin Haitao Zhao Jibo Wei Fangwei Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Peng Cheng Laboratory Shenzhen 518066China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China The School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China School of Humanities Beijing University of Posts and TelecommunicationsBeijing 100876China

The sixth generation(6G)mobile networks will reshape the world by offering instant,efficient,and intelligent hyper-connectivity,as envisioned by the previously proposed Ubiquitous-X 6G *** hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks,calling for revolutionary theories and technological *** this end,we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network(WePCN)vision for the Ubiquitous-X 6G *** particular,we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework,namely semantic base,and then establishing an intelligent and efficient semantic communication(IE-SC)network *** the IE-SC architecture,a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer,network protocol layer,and application-intent layer via semantic information *** proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in *** features a lower bandwidth requirement,less redundancy,and more accurate intent *** also present a brief review of recent advances in semantic communications and highlight potential use cases,complemented by a range of open challenges for 6G.

关键词： 6G semantic information semantic communication Intelligent communication

MIMS:Towards a Message Interface Based Memory System

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Journal of Computer Science & Technology 2014年第2期29卷 255-272页

作者：陈荔城陈明宇阮元黄永兵崔泽汉卢天越包云岗 State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences

The decades-old synchronous memory bus interface has restricted many innovations in the memory system, which is facing various challenges （or walls） in the era of multi-core and big data. In this paper, we argue that a message- based interface should be adopted to replace the traditional bus-based interface in the memory system. A novel message interface based memory system called MIMS is proposed. The key innovation of MIMS is that processors communicate with the memory system through a universal and flexible message packet interface. Each message packet is allowed to encapsulate multiple memory requests （or commands） and additional semantic information. The memory system is more intelligent and active by equipping with a local buffer scheduler, which is responsible for processing packets, scheduling memory requests, preparing responses, and executing specific commands with the help of semantic information. Under the MIMS framework, many previous innovations on memory architecture as well as new optimization opportunities such as address compression and continuous requests combination can be naturally incorporated. The experimental results on a 16-core cycle-detailed simulation system show that： with accurate granularity message, MIMS can improve system performance by 53.21% and reduce energy delay product （EDP） by 55.90%. Furthermore, it can improve effective bandwidth utilization by 62.42% and reduce memory access latency by 51% on average.

关键词： message interface memory system asynchronous granularity semantic information

An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) semantic information Real-time application Improved pulse coupled neural network Image segmentation

Enhancing N-Gram Based Metrics with semantics for Better Evaluation of Abstractive Text Summarization

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Journal of Computer Science & Technology 2022年第5期37卷 1118-1133页

作者： Jia-Wei He Wen-Jun Jiang Guo-Bang Chen Yu-Quan Le Xiao-Fei Ding College of information Science and Electronic Engineering Hunan UniversityChangsha 410082China

Text summarization is an important task in natural language processing and it has been applied in many ***,abstractive summarization has attracted many ***,the traditional evaluation metrics that consider little semantic information,are unsuitable for evaluating the quality of deep learning based abstractive summarization models,since these models may generate new words that do not exist in the original ***,the out-of-vocabulary(OOV)problem that affects the evaluation results,has not been well solved *** address these issues,we propose a novel model called ENMS,to enhance existing N-gram based evaluation metrics with *** be specific,we present two types of methods:N-gram based semantic Matching(NSM for short),and N-gram based semantic Similarity(NSS for short),to improve several widely-used evaluation metrics including ROUGE(Recall-Oriented Understudy for Gisting Evaluation),BLEU(Bilingual Evaluation Understudy),*** and NSS work in different *** former calculates the matching degree directly,while the latter mainly improves the similarity *** we propose an N-gram representation mechanism to explore the vector representation of N-grams(including skip-grams).It serves as the basis of our ENMS model,in which we exploit some simple but effective integration methods to solve the OOV problem *** results over the TAC AESOP dataset show that the metrics improved by our methods are well correlated with human judgements and can be used to better evaluate abstractive summarization methods.

关键词： summarization evaluation abstractive summarization hard matching semantic information

Semisupervised learning-based depth estimation with semantic inference guidance

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Science China(Technological Sciences) 2022年第5期65卷 1098-1106页

作者： ZHANG Yan FAN XiaoPeng ZHAO DeBin Department of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Depth estimation is a fundamental computer vision problem that infers three-dimensional(3D)structures from a given *** it is an ill-posed problem,to fit the projection function from the given scene to the 3D structure,traditional methods generally require mass amounts of annotated *** pixel-level annotation is quite labor consuming,especially when addressing reflective surfaces such as mirrors or *** widespread application of deep learning further intensifies the demand for large amounts of annotated ***,it is urgent and necessary to propose a framework that is able to reduce the requirement on the amount of *** this paper,we propose a novel semisupervised learning framework to infer the 3D structure from the given ***,semantic information is employed to make the depth inference more ***,we make both the depth estimation and semantic segmentation coarse-to-fine frameworks;thus,the depth estimation can be gradually guided by semantic *** compare our model with state-of-the-art *** experimental results demonstrate that our method is better than many supervised learning-based methods,which proves the effectiveness of the proposed method.

关键词： depth estimation semisupervised learning semantic information neural networks

UGC-YOLO:Underwater Environment Object Detection Based on YOLO with a Global Context Block

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Journal of Ocean University of China 2023年第3期22卷 665-674页

作者： YANG Yuyi CHEN Liang ZHANG Jian LONG Lingchun WANG Zhenfei School of information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China

With the continuous development and utilization of marine resources,the underwater target detection has gradually become a popular research topic in the field of underwater robot operations and target ***,it is difficult to combine the environmental semantic information and the semantic information of targets at different scales by detection algorithms due to the complex underwater *** this paper,a cascade model based on the UGC-YOLO network structure with high detection accuracy is *** YOLOv3 convolutional neural network is employed as the baseline *** fusing the global semantic information between two residual stages in the parallel structure of the feature extraction network,the perception of underwater targets is improved and the detection rate of hard-to-detect underwater objects is ***,the deformable convolution is applied to capture longrange semantic dependencies and PPM pooling is introduced in the highest layer network for aggregating semantic ***,a multi-scale weighted fusion approach is presented for learning semantic information at different *** are conducted on an underwater test dataset and the results have demonstrated that our proposed algorithm could detect aquatic targets in complex degraded underwater *** with the baseline network algorithm,the Common Objects in Context(COCO)evaluation metric has been improved by 4.34%.

关键词： object detection underwater environment semantic information semantic features deep learning algorithm

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