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Text-Image Feature Fine-Grained Learning for Joint Multimodal Aspect-Based Sentiment Analysis

Computers, Materials & Continua 2025年第1期82卷 279-305页

作者： Tianzhi Zhang Gang Zhou Shuang Zhang Shunhang Li Yepeng Sun Qiankun Pi Shuo Liu School of Data and Target Engineering Information Engineering UniversityZhengzhou450001China Information Engineering Department Liaoning Provincial College of CommunicationsShenyang110122China School of Computer and Artificial intelligence Zhengzhou UniversityZhengzhou450000China

Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal *** this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for ***,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect ***,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature ***,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and *** on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.

关键词： Multimodal sentiment analysis aspect-based sentiment analysis feature fine-grained learning graph convolutional network adjective-noun pairs

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A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

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Digital Communications and Networks 2022年第6期8卷 976-983页

作者： Ziming Liu Yang Xu Cheng Zhang Haroon Elahi Xiaokang Zhou College of Information Science and Engineering Northeastern UniversityShenyang110819China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Zhejiang UniversityHangzhou310058China Department of Computing Science Umea UniversityUmea90187Sweden Faculty of Data Science Shiga UniversityHikone522-8522Japan riken center for advanced intelligence project RIKENTokyo103-0027Japan

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric ***,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust *** this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch *** on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social *** use blockchain to record power trading data for trusted pricing and use smart contracts for transaction *** simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

关键词： Social internet of vehicles Bl ockchain Collaborative power trading Vehicle-to-vehicle charging 5G

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Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

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Computers, Materials & Continua 2025年第1期82卷 345-369页

作者： Jiaying Shen Donglin Zhu Yujia Liu Leyi Wang Jialing Hu Zhaolong Ouyang Changjun Zhou Taiyong Li School of Computer Science and Technology Zhejiang Normal UniversityJinhua321004China School of Future Technologies Jiangxi Institute of Applied Science and TechnologyNanchang330000China School of Computing and Artificial intelligence Southwestern University of Finance and EconomicsChengdu611130China

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real *** development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of *** a specific area,achieving higher signal coverage with fewer base stations has become an urgent ***,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as *** a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by *** also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization *** better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base *** experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches *** results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and *** ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

关键词： Particle swarm optimization effective coverage area global optimization base station deployment

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Multidimensional clinical data denoising via Bayesian CP factorization

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Science China(Technological Sciences) 2020年第2期63卷 249-254页

作者： CUI GaoChao ZHU Li GUI LiHua ZHAO QiBin ZHANG JianHai CAO JianTing Graduate School of Engineering Saitama Institute of TechnologySaitama 3690293Japan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Cognitive Science Department School of Information Science and EngineeringXiamen UniversityXiamen 361005China Tensor Learning Unit Center for Advanced Intelligence ProjectRIKENSaitama 1030027Japan School of Automation Guangdong University of TechnologyGuangzhou 510006China

CANDECOMP/PARAFAC(CP) tensor factorization is an efficient technique for incomplete tensor-data processing through capturing the multilinear latent factors. Based on the incorporate a sparsity-inducing prior over multiple latent factors and appropriate hyper-priors over all hyper-parameters, a Bayesian-based hierarchical probabilistic CP factorization model could be formed. By this way, the rank of the incomplete tensor can be determined automatically. In this paper, we explored the tensor completion method in processing incomplete multidimensional electroencephalogram(EEG) and magnetic resonance imaging(MRI) clinical data. The empirical results indicated that the Bayesian CP tensor factorization of incomplete data method can effectively recover EEG signal with missing data and denoised the noisy MRI data.

关键词： electroencephalogram(EEG) magnetic resonance imaging(MRI) Bayesian tensor factorization CANDECOMP/PARAFAC(CP)

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Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 405-423页

作者： Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Faculty of Artificial intelligence Menoufia UniversityShebin El-Koom32511Egypt

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and *** seal inspection is commonly audited manually to ensure document ***,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the *** image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image ***,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp ***,the fixed training data categories make handling new categories to be a challenging *** paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these ***,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese ***,we compare the results with the pre-stored standard seal template images in the database to obtain the seal *** evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in *** proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation ***,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关键词： Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network

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Tuning the ferroelectricity of Hf0.5Zr0.5O2 with alloy electrodes

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Science China(Information Sciences) 2024年第8期67卷 321-330页

作者： Keqin LIU Bingjie DANG Zhiyu YANG Teng ZHANG Zhen YANG Jinxuan BAI Zelun PAN Ru HUANG Yuchao YANG Beijing advanced Innovation center for Integrated Circuits School of Integrated Circuits Peking University School of Electronic and Computer Engineering Peking University center for Brain Inspired Chips Institute for Artificial Intelligence Peking University center for Brain Inspired intelligence Chinese Institute for Brain ResearchBeijing

Tuning ferroelectricity of Hf0.5Zr0.5O2is crucial for facilitating its practical applications in various fields, including in-memory and neuromorphic computing. Previous studies have revealed that the electrodes have a significant influence on ferroelectricity, and changing electrode materials can realize different but discrete ferroelectric polarization values. Here, we introduce an alloy-electrode method, in order to achieve gradual and accurate modulation of ferroelectric polarization, especially useful for matching the polarization charges at the interface of ferroelectric insulators and ferroelectric semiconductors. Au and W electrodes are chosen as baselines for realizing weak and strong ferroelectric polarization, where the intermediate states can be achieved by adjusting the ratio of metals in the Au-W alloy. To demonstrate the generality of this approach, the Cu-W alloy electrode is also realized for tuning ferroelectric polarization. The effect of alloy electrodes on device leakage current, endurance, and retention is evaluated. In addition, the temperature stability of ferroelectric capacitors is tested, where limited changes in both remnant polarization and coercive voltages are observed, showing the great potential of the ferroelectric hafnium oxide. Such gradual modulation of ferroelectric polarization could facilitate the application of Hf0.5Zr0.5O2in in-memory and neuromorphic computing.

关键词： ferroelectric hafnium oxide alloy electrode gradual modulation temperature stability

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Memristive dynamics enabled neuromorphic computing systems

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Science China(Information Sciences) 2023年第10期66卷 5-19页

作者： Bonan YAN Yuchao YANG Ru HUANG center for Brain Inspired Chips Institute for Artificial Intelligence Peking University Beijing advanced Innovation center for Integrated Circuits School of Integrated Circuits Peking University School of Electronic and Computer Engineering Peking University center for Brain Inspired intelligence Chinese Institute for Brain Research(CIBR)

The slowing down of transistor scaling and explosive growth for intelligence computing power emerge as the two driving factors for the study of novel devices and materials to pursue highly-efficient computing systems. Memristors, incorporating rich intrinsic dynamics, are a promising candidate for constructing efficient and scalable bio-inspired computing systems. In this progress report, we review the latest advances in novel types of memristors as well as their applications in implementing neuromorphic computing systems. This paper not only covers the memristive dynamics-enabled bionic computing systems but also discusses the memristive sensory systems that integrate sensing and computing. Eventually, device-circuit co-optimization methods are given to emphasize the trend of cross-layer co-design in this fast-evolving *** innovation in memristor devices mainly focuses on specialized computing hardware and yields superior computing and sensing efficiency. At last, we offer our insight into the trend of state-of-the-art research in memristive materials, devices, circuits, and systems.

关键词： memrsitor memristive dynamics artificial synapse artificial neuron neuromorphic computing

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Enriching the Transfer Learning with Pre-Trained Lexicon Embedding for Low-Resource Neural Machine Translation

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Tsinghua Science and Technology 2022年第1期27卷 150-163页

作者： Mieradilijiang Maimaiti Yang Liu Huanbo Luan Maosong Sun the Institute for Artificial intelligence Beijing National Research Center for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua UniversityBeijing 100084China the Beijing Academy of Artificial intelligence Beijing Advanced Innovation Center for Language ResourcesBeijing 100084China

Most State-Of-The-Art(SOTA) Neural Machine Translation(NMT) systems today achieve outstanding results based only on large parallel *** large-scale parallel corpora for high-resource languages is easily ***,the translation quality of NMT for morphologically rich languages is still unsatisfactory,mainly because of the data sparsity problem encountered in Low-Resource Languages(LRLs).In the low-resource NMT paradigm,Transfer Learning(TL) has been developed into one of the most efficient *** is difficult to train the model on high-resource languages to include the information in both parent and child models,as well as the initially trained model that only contains the lexicon features and word embeddings of the parent model instead of the child languages *** this work,we aim to address this issue by proposing the language-independent Hybrid Transfer Learning(HTL) method for LRLs by sharing lexicon embedding between parent and child languages without leveraging back translation or manually injecting ***,we train the High-Resource Languages(HRLs) as the parent model with its ***,we combine the parent and child language pairs using the oversampling method to train the hybrid model initialized by the previously parent ***,we fine-tune the morphologically rich child model using a hybrid ***,we explore some exciting discoveries on the original TL *** results show that our model consistently outperforms five SOTA methods in two languages Azerbaijani(Az) and Uzbek(Uz).Meanwhile,our approach is practical and significantly better,achieving improvements of up to 4:94 and 4:84 BLEU points for low-resource child languages Az ! Zh and Uz ! Zh,respectively.

关键词： artificial intelligence natural language processing neural network machine translation low-resource languages transfer learning

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Now and future of mouse mutagenesis for human disease models

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Journal of Genetics and Genomics 2010年第9期37卷 559-572页

作者： Yoichi Gondo Mutagenesis and Genomics Team RIKEN Bio Resource Center

One of the major objectives of the Human Genome project is to understand the biological function of the gene and genome as well as to develop clinical applications for human diseases. For this purpose, the experimental validations and preclinical trails by using animal models are indispensable. The mouse （Mus musculus） is one of the best animal models because genetics is well established in the mouse and embryonic manipulation technologies are also well developed. Large-scale mouse mutagenesis projects have been conducted to develop various mouse models since 1997. Originally, the phenotype-driven mutagenesis with N-ethyl-N-nitrosourea （ENU） has been the major efforts internationally then knockout/conditional mouse projects and gene-driven mutagenesis have been following. At the beginning, simple monogenic traits in the experimental condition have been elucidated. Then, more complex traits with variety of environmental interactions and gene-to-gene interactions （epistasis） have been challenged with mutant mice. In addition, chromosomal substitution strains and collaborative cross strains are also available to elucidate the complex traits in the mouse. Altogether, mouse models with mutagenesis and various laboratory strains will accelerate the studies of functional genomics in the mouse as well as in human.

关键词： mutagenesis functional genomics reverse genetics model animal quantitative trait epistasis

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Morphomics via next-generation electron microscopy

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Journal of Molecular Cell Biology 2023年第12期15卷 12-27页

作者： Raku Son Kenji Yamazawa Akiko Oguchi Mitsuo Suga Masaru Tamura Motoko Yanagita Yasuhiro Murakawa Satoshi Kume riken-IFOM Joint Laboratory for Cancer Genomics RIKEN Center for Integrative Medical SciencesYokohama 230-0045Japan Department of Nephrology Graduate School of MedicineKyoto UniversityKyoto 606-8507Japan advanced Manufacturing Support Team RIKEN Center for Advanced PhotonicsWako 351-0198Japan Multimodal Microstructure Analysis Unit RIKEN–JEOL Collaboration CenterKobe 650-0047Japan Technology and Development Team for Mouse Phenotype Analysis RIKEN BioResource Research CenterTsukuba 305-0074Japan Institute for the advanced Study of Human Biology(ASHBi) Kyoto UniversityKyoto 606-8501Japan IFOM—The FIRC Institute of Molecular Oncology Milan 20139Italy Laboratory for Pathophysiological and Health Science RIKEN Center for Biosystems Dynamics ResearchKobe 650-0047Japan center for Health Science Innovation Osaka City UniversityOsaka 530-0011Japan Osaka Electro-Communication University Neyagawa 572-8530Japan

The living body is composed of innumerable fine and complex *** these structures have been studied in the past,a vast amount of information pertaining to them still remains *** attempting to observe these ultra-structures,the use of electron microscopy(EM)has become ***,conventional EM settings are limited to a narrow tissue area,which can bias ***,new trends in EM research have emerged,enabling coverage of far broader,nano-scale fields of view for two-dimensional wide areas and three-dimensional large ***,cutting-edge bioimage informatics conducted via deep learning has accelerated the quantification of complex morphological *** together,these technological and analytical advances have led to the comprehensive acquisition and quantification of cellular morphology,which now arises as a new omics science termed‘morphomics’.

关键词： comprehensive morphological analysis next-generation electron microscopy 3D bioimaging imaging database deep learning

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