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

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

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Survey on cloud model based similarity measure of uncertain concepts

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CAAI Transactions on Intelligence Technology 2019年第4期4卷 223-230页

作者： Shuai Li guoyin wang Jie Yang Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsNo.2 Chongwen StreetNan'an DistrictChongqingPeople’s Republic of China

It is a basic task to measure the similarity between two uncertain concepts in many real-life artificial intelligence applications,such as image retrieval,collaborative filtering,public opinion guidance,and so *** an important cognitive computing model,cloud model has been used in many fields of artificial *** can realise the bidirectional cognitive transformation between qualitative concept and quantitative data based on the theory of probability and fuzzy *** similarity measure of two uncertain concepts is a fundamental issue in cloud model *** similarity measure methods of cloud model are surveyed in this *** limitations are analysed in *** related future research topics are proposed.

关键词： artificial similarity cloud

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Factors affecting forensic electric network frequency matching-A comprehensive study

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Digital Communications and Networks 2024年第4期10卷 1121-1130页

作者： Guang Hua Qingyi wang Dengpan Ye Haijian Zhang guoyin wang Shuyin Xia School of Electronic Information Wuhan UniversityWuhan430072China Institute for Infocomm Research(I2R) Agency for ScienceTechnology and Research(A*STAR)Singapore138632Singapore School of Cyber Science and Engineering Wuhan UniversityWuhan430072China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China

The power system frequency fluctuations could be captured by digital recordings and extracted to compare with a reference database for forensic timestamp *** is known as the Electric Network Frequency(ENF)criterion,enabled by the properties of random fluctuations and intra-grid *** essence,this is a task of matching a short random sequence within a long reference,whose accuracy is mainly concerned with whether this match could be uniquely *** this paper,we comprehensively analyze the factors affecting the reliability of ENF matching,including the length of test recording,length of reference,temporal resolution,and Signal-to-Noise Ratio(SNR).For synthetic analysis,we incorporate the first-order AutoRegressive(AR)ENF model and propose an efficient Time-Frequency Domain noisy ENF synthesis ***,the reliability analysis schemes for both synthetic and real-world data are respectively *** a comprehensive study,we quantitatively reveal that while the SNR is an important external factor to determine whether timestamp verification is viable,the length of test recording is the most important inherent factor,followed by the length of ***,the temporal resolution has little impact on ***,a practical workflow of the ENF-based audio timestamp verification system is proposed,incorporating the discovered results.

关键词： Digital forensics Audio forensics Data authentication Timestamp verification Electric network frequency criterion

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A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks

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Big Data Mining and Analytics 2018年第3期1卷 245-256页

作者： Ziling Pang guoyin wang Jie Yang Chongqing Key Laboratory of Computational Intelligence Chongqing University of Post and Telecommunication

There are many algorithms for solving complex problems in supervised manner. However, unsupervised tasks are more common in real scenarios. Inspired by the idea of granular computing and the characteristics of human cognitive process, this paper proposes a complex tasks decomposition mechanism based on Density Peaks Clustering(DPC) to address complex tasks with an unsupervised process, which simulates the multi-granular observation and analysis of human being. Firstly, the DPC algorithm is modified to nullify its essential defects such as the difficulty of locating correct clustering centers and classifying them accurately. Then, the improved DPC algorithm is used to construct the initial decomposition solving space with multi-granularity theory. We also define subtask centers set and the granulation rules to guide the multi-granularity decomposing procedure. These rules are further used to decompose the solving space from coarse granules to the optimal fine granules with a convergent and automated process. Furthermore, comprehensive experiments are presented to verify the applicability and veracity of our proposed method in community-detection tasks with several benchmark complex social *** results show that our method outperforms other four state-of-the-art approaches.

关键词： multi-granularity task decomposition density peaks complex network

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Rethinking the image feature biases exhibited by deep convolutional neural network models in image recognition

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CAAI Transactions on Intelligence Technology 2022年第4期7卷 721-731页

作者： Dawei Dai Yutang Li Yuqi wang Huanan Bao guoyin wang College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingChina

In recent years,convolutional neural networks(CNNs)have been applied successfully in many ***,these deep neural models are still considered as“black box”for most *** of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how CNNs process these *** is widely believed that CNN models combine low‐level features to form complex shapes until the object can be readily classified,however,several recent studies have argued that texture features are more important than other *** this paper,we assume that the importance of certain features varies depending on specific tasks,that is,specific tasks exhibit feature *** designed two classification tasks based on human intuition to train deep neural models to identify the anticipated *** designed experiments comprising many tasks to test these biases in the Res Net and Dense Net *** the results,we conclude that(1)the combined effect of certain features is typically far more influential than any single feature;(2)in different tasks,neural models can perform different biases,that is,we can design a specific task to make a neural model biased towards a specific anticipated feature.

关键词： CNNs features understandable models

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D-BERT: Incorporating dependency-based attention into BERT for relation extraction

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CAAI Transactions on Intelligence Technology 2021年第4期6卷 417-425页

作者： Yuan Huang Zhixing Li Wei Deng guoyin wang Zhimin Lin Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqingChina Centet of Statistical Research Southwestern Univetsity of Finance and EconomicsChengduChina

Relation extraction between entity pairs is an increasingly critical area in natural language *** the pre-trained bidirectional encoder representation from transformer(BERT)performs excellendy on the text classification or sequence labelling ***,the high-level syntactic features that consider the dependency between each word and the target entities into the pre-trained language models are *** model also utilizes the intermediate layers of BERT to acquire different levels of semantic information and designs multi-granularity features for final relation *** model offers a momentous improvement over the published methods for the relation extraction on the widely used data sets.

关键词： BERT relation processing.

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Intelligent resource allocation in mobile blockchain for privacy and security transactions: a deep reinforcement learning based approach

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Science China(Information Sciences) 2021年第6期64卷 172-187页

作者： Zhaolong NING Shouming SUN Xiaojie wang Lei GUO guoyin wang Xinbo GAO Ricky Y.K.KWOK School of Communication and Information Engineering Chongqing University of Posts and Telecommunications School of Software Dalian University of Technology State Key Laboratory of Industrial Control Technology Zhejiang University Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Department of Electrical and Electronic Engineering University of Hong Kong

In order to protect the privacy and data security of mobile devices during the transactions in the industrial Internet of Things(IIoT), we propose a mobile edge computing(MEC)-based mobile blockchain framework by considering the limited bandwidth and computing power of small base stations(SBSs). First,we formulate a joint bandwidth and computing resource allocation problem to maximize the long-term utility of all mobile devices, and take into account the mobility of devices as well as the blockchain throughput. We decompose the formulated problem into two subproblems to decrease the dimension of action space. Then,we propose a deep reinforcement learning additional particle swarm optimization(DRPO) algorithm to solve the two subproblems, in which a particle swarm optimization algorithm is leveraged to avoid the unnecessary search of a deep deterministic policy gradient approach. Simulation results demonstrate the effectiveness of our method from various aspects.

关键词： mobile blockchain deep reinforcement learning mobile edge computing power allocation bandwidth allocation

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SMDP-based sleep policy for base stations in heterogeneous cellular networks

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Digital Communications and Networks 2021年第1期7卷 120-130页

作者： Jing Wu Yun Li Hongcheng Zhuang Zhiwen Pan guoyin wang Yongju Xian College of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065P.R.China Chongqing Key Laboratory of Mobile Communications Technology Chongqing University of Posts and TelecommunicationsChongqing400065P.R.China Huawei Technologies Co.Ltd. Shenzhen518129P.R.China Naitional Mobile Communications Research Laboratory Southeast UniversityNanjingJiangsu210096P.R.China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing400065P.R.China

A dense heterogeneous cellular network can effectively increase the system capacity and enhance the network *** is a key technology for the new generation of the mobile communication *** dense deployment of small base stations not only improves the quality of network service,but also brings about a significant increase in network energy *** paper mainly studies the energy efficiency optimization of the Macro-Femto heterogeneous cellular *** the dynamic random changes of the access users in the network,the sleep process of the Femto Base Stations(FBSs)is modeled as a Semi-Markov Decision Process(SMDP)model in order to save the network energy *** further,this paper gives the dynamic sleep algorithm of the FBS based on the value *** simulation results show that the proposed SMDP-based adaptive sleep strategy of the FBS can effectively reduce the network energy consumption.

关键词： Macro-femto heterogeneous network Base station sleep Energy consumption

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Rough set extensions in incomplete information systems

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Frontiers of Electrical and Electronic Engineering in China 2008年第4期3卷 399-405页

作者： guoyin wang Lihe GUAN Feng HU School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031China Institute of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Information and Calculation Science Chongqing Jiaotong UniversityChongqing 400074Chin

All eight possible extended rough set models in incomplete information systems are *** analyzing existing extended models and technical meth-ods of rough set theory,the strategy of model extension is found to be suitable for processing incomplete information systems instead of filling possible values for missing *** analyzing the definitions of existing extended models,a new general extended model is *** new model is a generalization of indiscernibility relations,tolerance relations and non-symmetric similarity ***,suggestions for further study of rough set theory in incomplete informa-tion systems are put forward.

关键词： rough set incomplete information system extended model

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Guest Editorial: Rough Sets and Data Mining

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CAAI Transactions on Intelligence Technology 2019年第4期4卷 201-202页

作者： HIROSHI SAKAI MICHINORI NAKATA WEI-ZHI WU DUOQIAN MIAO guoyin wang Faculty of Engineering Kyushu Institute of TechnologySensuiTobataKitakyushu 804-8550Japan Faculty of Management and Information Science Josai International UniversityToganeChiba 283-0002Japan School of Mathematics Physics and Information ScienceZhejiang Ocean UniversityZhoushanZhejiang316022China Department of Computer Science and Technology Tongji UniversityShanghai201804China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqingChina

A rough set,first described by Polish computer scientist Zdzis?aw Pawlak,is a formal approximation of a crisp set,and it is now known as a new mathematical tool to process vague *** are used for machine learning,knowledge discovery,feature selection,etc.,and are applied to artificial intelligence,medical informatics,civil engineering,Kansei engineering,decision science,business administration,and so ***,research on data mining using rough sets is widely spreading,and the obtained association rules are applied to the characterisation of data and decision support.

关键词： Rough artificial computer

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