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Uncertain knowledge graph embedding:an effective method combining multi-relation and multi-path

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Frontiers of Computer Science 2024年第3期18卷 73-89页

作者： Qi LIU Qinghua ZHANG Fan ZHAO Guoyin WANG Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and TourismChongqing University of Posts and TelecommunicationsChongqing 400065China chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China chongqing Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China

Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge *** research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this ***,there are still unresolved *** the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information *** the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be *** address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this ***,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is ***,the asymmetry in the UKG is *** embed asymmetric relation facts of UKG,a multi-relation embedding model is ***,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of *** results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.

关键词： knowledge representation uncertain knowledge graph multi-relation embedding uncertain reasoning

A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 255-273页

作者： Zhong Qu Guoqing Mu Bin Yuan School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this ***,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage *** limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile *** solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature ***,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of *** addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context ***,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction *** evaluate our method on three public crack datasets:DeepCrack,CFD,and *** results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight cr

关键词： Shallow feature extraction module large kernel atrous convolution dual encoder lightweight network crack detection

Leveraging Uncertainty for Depth-Aware Hierarchical Text Classification

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CMC-COMPUTERS MATERIALS & CONTINUA 2024年第3期80卷 4111-4127页

作者： Wu, Zixuan Wang, Ye Shen, Lifeng Hu, Feng Yu, Hong chongqing univ posts & telecommun Chongqing Key Lab Computat Intelligence Chongqing 400000 Peoples R China Hong Kong univ Sci & Technol Div Emerging Interdisciplinary Areas Hong Kong 999077 Peoples R China

Hierarchical Text Classification (HTC) aims to match text to hierarchical labels. Existing methods overlook two critical issues: first, some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target. Second, error propagation occurs when a misclassification at a parent node propagates down the hierarchy, ultimately leading to inaccurate predictions at the leaf nodes. To address these limitations, we propose an uncertainty-guided HTC depth-aware model called DepthMatch. Specifically, we design an early stopping strategy with uncertainty to identify incomplete matching between text and labels, classifying them into the corresponding parent node labels. This approach allows us to dynamically determine the classification depth by leveraging evidence to quantify and accumulate uncertainty. Experimental results show that the proposed DepthMatch outperforms recent strong baselines on four commonly used public datasets: WOS (Web of Science), RCV1-V2 (Reuters Corpus Volume I), AAPD (Arxiv Academic Paper Dataset), and BGC. Notably, on the BGC dataset, it improves Micro-F1 and Macro-F1 scores by at least 1.09% and 1.74%, respectively.

关键词： Hierarchical text classification incomplete text-label matching uncertainty depth-aware early stopping strategy

Actor-Critic-Based UAV-Assisted Data Collection in the Wireless Sensor Network

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China Communications 2024年第4期21卷 163-177页

作者： Huang Xiaoge Wang Lingzhi He Yong Chen Qianbin School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

Wireless Sensor Network(WSN)is widely utilized in large-scale distributed unmanned detection scenarios due to its low cost and flexible ***,WSN data collection encounters challenges in scenarios lacking communication *** aerial vehicle(UAV)offers a novel solution for WSN data collection,leveraging their high *** this paper,we present an efficient UAV-assisted data collection algorithm aimed at minimizing the overall power consumption of the ***,a two-layer UAV-assisted data collection model is introduced,including the ground and aerial *** ground layer senses the environmental data by the cluster members(CMs),and the CMs transmit the data to the cluster heads(CHs),which forward the collected data to the *** aerial network layer consists of multiple UAVs that collect,store,and forward data from the CHs to the data center for ***,an improved clustering algorithm based on K-Means++is proposed to optimize the number and locations of ***,an Actor-Critic based algorithm is introduced to optimize the UAV deployment and the association with ***,simulation results verify the effectiveness of the proposed algorithms.

关键词： actor critic data collection deep reinforcement learning unmanned aerial vehicle wireless sensor network

GNN-based temporal knowledge reasoning for UAV mission planning systems

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The Journal of China universities of posts and telecommunications 2024年第1期31卷 12-25页

作者： Chai Rong Duan Xiaofang Wang Lixuan School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

Unmanned aerial vehicles(UAVs)are increasingly applied in various mission scenarios for their versatility,scalability and *** UAV mission planning systems(UMPSs),an efficient mission planning strategy is essential to meet the requirements of UAV ***,rapidly changing environments and unforeseen threats pose challenges to UMPSs,making efficient mission planning *** address these challenges,knowledge graph technology can be utilized to manage the complex relations and constraints among UAVs,missions,and *** paper investigates knowledge graph application in UMPSs,exploring its modeling,representation,and storage concepts and ***,the construction of a specialized knowledge graph for UMPS is ***,the paper delves into knowledge reasoning within UMPSs,emphasizing its significance in timely updates in the dynamic environment.A graph neural network(GNN)-based approach is proposed for knowledge reasoning,leveraging GNNs to capture structural information and accurately predict missing entities or relations in the knowledge *** relation reasoning,path information is also incorporated to improve the accuracy of *** account for the temporal dynamics of the environment in UMPS,the influence of timestamps is captured through the attention *** effectiveness and applicability of the proposed knowledge reasoning method are verified via simulations.

关键词： unmanned aerial vehicle mission planning knowledge graph knowledge reasoning graph neural network

Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks

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China Communications 2024年第4期21卷 53-73页

作者： Feng Chuan Zhang Xu Han Pengchao Ma Tianchun Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communication and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/***, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

关键词： benefit maximization deep reinforcement learning multi-access edge cloud task offloading

An object detection approach with residual feature fusion and second-order term attention mechanism

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 411-424页

作者： Cuijin Li Zhong Qu Shengye Wang College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingChina College of Electronic Information Chongqing Institute of EngineeringChongqingChina

Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging *** the boundary box location is not sufficiently accurate and it is difficult to distinguish overlapping and occluded objects,the authors propose a network model with a second-order term attention mechanism and occlusion ***,the backbone network is built on *** a method is designed for the feature extraction network based on an item-wise attention mechanism,which uses the filtered weighted feature vector to replace the original residual fusion and adds a second-order term to reduce the information loss in the process of fusion and accelerate the convergence of the ***,an objected occlusion regression loss function is studied to reduce the problems of missed detections caused by dense *** experimental results demonstrate that the authors’method achieved state-of-the-art performance without reducing the detection *** mAP@.5 of the method is 85.8%on the Foggy_cityscapes dataset and the mAP@.5 of the method is 97.8%on the KITTI dataset.

关键词： artificial intelligence computer vision image processing machine learning neural network object recognition

An improved construction algorithm of polar codes based on the frozen bits

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Optoelectronics Letters 2024年第3期20卷 157-162页

作者： YUAN Jianguo ZHANG Fengguo YU Linfeng PANG Yu School of Optoelectronic Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

In order to improve the problems that the minimum hamming weight(MHW) of the polar codes of the traditional Gaussian approximation(GA) construction is small and its performance is not good enough, an improved channel construction algorithm of polar codes based on frozen bits is proposed by combining the construction of the Reed-Muller(RM) code to effectively increase the MHW and analyzing the correcting and checking functions of the frozen bits in the successive cancellation list(SCL) decoding.

关键词： polar algorithm successive

Constraints on real scalar inflation from preheating using LATTICEEASY

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Chinese Physics C 2024年第6期48卷 240-246页

作者：程伟秦通姜玖江周瑞雨 School of Science Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Physics and chongqing Key Laboratory for Strongly Coupled Physics Chongqing UniversityChongqing 401331China

In this paper,we undertake a detailed study of real scalar inflation using LATTICEEASY simulations to investigate preheating ***,the scalar inflation potential with non-minimal coupling can be approximated using a quartic *** observe that the evolutionary behavior of this potential remains unaffected by the coupling ***,the theoretical predictions for the scalar spectral index(n_(s))and tensor-toscalar power ratio(r)are independent of this ***,the coefficients of this model are not constrained by Planck ***,the properties of preheating after inflation provide a viable approach to examining these *** LATTICEEASY simulations,we trace the evolution of particle number density,scale factor,and energy density during the preheating ***,we derive the parameters,such as the energy ratio(γ)and the e-folding number of preheating(N_(pre)),which facilitate further predictions of n s and *** successfully validate real scalar inflation model using preheating in LATTICEEASY simulations based on the analytical relationship between preheating and inflation models.

关键词： inflation latticeeasy preheating