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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Jing, Zhang Weijie, Ding Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Aiming at the high frequency of urban traffic accidents and the relative shortage of police resources, a multi-objective discrete Jaya algorithm is proposed to solve the problem of police resource deployment in emergency traffic accident handling. The purpose is to make timely response and handling of traffic accidents through the rational allocation of police deployment points in the jurisdiction. Taking the coverage of accident points and the average response time of accidents as the optimization objectives, the discrete Jaya algorithm is used as the global search strategy to approach the Pareto front by constantly approaching the optimal solution and far away from the worst solution, and then the neighborhood search strategy is used to improve the local search ability of the algorithm. Finally, a practical example is given to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Law enforcement

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Research on rock hand specimen naming method based on deep learning and Inception-v3 model

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Global Geology 2024年第4期27卷 196-206页

作者： LI SiJia SHEN YanJie QIAN Ye College of Earth Sciences Jilin UniversityChangchun 130061China Shandong Provincial Engineering Laboratory of Application and Development for Big Data of Deep Gold Exploration Weihai 264209ShandongChina Key Laboratory of Mineral Resources Evaluation in Northeast Asia Ministry of Nature ResourcesChangchun 130061China

The naming of rock hand specimens is usually conducted by geological workers based on observed mineral composition,texture characteristics,etc.,combined with their own knowledge *** accuracy of identification results is limited by the experience,research interests,and identification level of the identifier,as well as the complexity of the rock *** improve the efficiency of rock hand specimen identification,this paper proposes a method for rock image recognition and classification based on deep learning and the Inception-v3 *** encompasses the preprocessing of collected photographs of typical intrusive rock hand specimens,along with augmenting the sample size through data augmentation methods,culminating in a comprehensive dataset comprising 12501 *** results show that the model has good learning ability when there is sufficient *** iterative training of the Inception-v3 model on the rock dataset,the accuracy of rock image recognition reaches 92.83%,with a loss of only ***,several common types of intrusive rocks can be identified:gabbro,granite,diorite,peridotite,granodiorite,diabase,and granite *** is developed for open use by geological workers to improve work efficiency.

关键词： Inception-v3 Keras deep learning image identification naming of the intrusive rocks specimen

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SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding

arXiv

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arXiv 2025年

作者： Shi, Liangtao Liu, Ting Hu, Xiantao Hu, Yue Yin, Quanjun Hong, Richang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230009 China Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China School of systems engineering National University of Defense Technology Hunan Province Changsha410073 China Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China

Visual grounding aims to ground an image region through natural language, which heavily relies on cross-modal alignment. Most existing methods transfer visual/linguistic knowledge separately by fully fine-tuning uni-modal pre-trained models, followed by a simple stack of visual-language transformers for multimodal fusion. However, these approaches not only limit adequate interaction between visual and linguistic contexts, but also incur significant computational costs. Therefore, to address these issues, we explore a step-wise multimodal fusion and adaption framework, namely SwimVG. Specifically, SwimVG proposes step-wise multimodal prompts (Swip) and cross-modal interactive adapters (CIA) for visual grounding, replacing the cumbersome transformer stacks for multimodal fusion. Swip can improve the alignment between the vision and language representations step by step, in a token-level fusion manner. In addition, weight-level CIA further promotes multimodal fusion by cross-modal interaction. Swip and CIA are both parameter-efficient paradigms, and they fuse the cross-modal features from shallow to deep layers gradually. Experimental results on four widely-used benchmarks demonstrate that SwimVG achieves remarkable abilities and considerable benefits in terms of efficiency. Our code is available at https://***/liuting20/SwimVG. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Differences in pseudogene evolution contributed to the contrasting flavors of turnip and Chiifu, two Brassica rapa subspecies

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Plant Communications 2023年第1期4卷 120-135页

作者： Xin Yin Danni Yang Youjie Zhao Xingyu Yang Zhili Zhou Xudong Sun Xiangxiang Kong Xiong Li Guangyan Wang Yuanwen Duan Yunqiang Yang Yongping Yang Plant Germplasm and Genomics Center Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of SciencesKunming 650204China Institute of Tibetan Plateau Research at Kunming Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunmingYunnanChina University of Chinese Academy of Sciences Beijing 100049China

Pseudogenes are important resources for investigation of genome evolution and genomic diversity because they are nonfunctional but have regulatory effects that influence plant adaptation and ***,few systematic comparative analyses of pseudogenes in closely related species have been ***,we present a turnip(Brassica rapa ***)genome sequence and characterize pseudogenes among diploid Brassica species/*** results revealed that the number of pseudogenes was greatest in Brassica oleracea(CC genome),followed by ***(AA genome)and then Brassica nigra(BB genome),implying that pseudogene differences emerged after species *** Brassica AA genomes,pseudogenes were distributed asymmetrically on chromosomes because of numerous chromosomal insertions/rearrangements,which contributed to the diversity among *** differences among subspecies were reflected in the flavor-related glucosinolate(GSL)***,turnip had the highest content of pungent substances,probably because of expansion of the methylthioalkylmalate synthase-encoding gene family in turnips;these genes were converted into pseudogenes in *** ***(Chiifu).RNA interference-based silencing of the gene encoding 2-oxoglutarate-dependent dioxygenase 2,which is also associated with flavor and anticancer substances in the GSL pathway,resulted in increased abundance of anticancer compounds and decreased pungency of turnip and *** findings revealed that pseudogene differences between turnip and Chiifu influenced the evolution of flavor-associated GSL metabolism-related genes,ultimately resulting in the different flavors of turnip and Chiifu.

关键词： turnip genome comparative genomics pseudogene evolution GSL biosynthesis flavor

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Research on Image Enhancement of Rural Orchard Security Monitoring under Low Illumination 22

Research on Image Enhancement of Rural Orchard Security Moni...

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Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer engineering

作者： Zili Zhou Shenming Gu School of Information Engineering Zhejiang Ocean University China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University China

ISBN: (纸本)9781450397148

It is great significance to improve the quality of the monitoring image for the low illumination situation and to improve the security of the rural orchard. In this paper, an improved block homomorphic filtering algorithm is proposed to improve the quality of video surveillance image due to low illuminance in real environment. On the basis of image block adaptation and according to different illumination conditions, the filtering effect is improved by adding and adjusting the weight factor of the transfer function, and the blocking edge effect is alleviated by smoothing filtering. In order to solve the problem of low saturation of low illumination image, the image saturation is adjusted hierarchically and the saturation lower than the threshold value is nonlinear pulled up, so as to restore the color saturation of the image as much as possible. The experimental results show that the PSNR, SSIM and IE are improved by 6%, 21% and 4% respectively. Ultimately, it can improve effectively the image quality of orchard video monitoring under low illumination.

关键词： Rural orchard, Improved block homomorphic filtering, Saturation stratification adjustment Monitoring image, Low illumination

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Fine-grained classification network for fish based on the attention mechanism and EfficientNet 22

Fine-grained classification network for fish based on the at...

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Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Yaohan Gong Shenming Gu Linting Guan School of Information Engineering Zhejiang Ocean University China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University China

ISBN: (纸本)9781450398343

Many countries have been interested in the sustainable development of marine resources. Countries can protect the sustainable development of fishery resources by joining international fisheries organizations such as CCSBT, IATTC, IOTC, ICCAT, and others, and by adhering to the Code of Conduct for Responsible Fisheries. Traditional fishing supervision methods, on the other hand, frequently have the issue that the amount of original data is cumbersome and expensive, which cannot be handled manually and wastes a lot of manpower. This paper investigates fine-grained fish classification methods based on deep learning and attention mechanisms and proposes one that can target complex background and actual working scenarios, allowing countries to reallocate human capital to management and law enforcement activities.

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CHARACTERIZATION OF DIM LIGHT RESPONSE IN DVS PIXEL: DISCONTINUITY OF EVENT TRIGGERING TIME

arXiv

引用

arXiv 2024年

作者： Jiang, Xiao Zhou, Fei College of Electronic and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication China Peng Cheng Laboratory Shenzhen China

Dynamic Vision Sensors (DVS) have recently generated great interest because of the advantages of wide dynamic range and low latency compared with conventional frame-based cameras. However, the complicated behaviors in dim light conditions are still not clear, restricting the applications of DVS. In this paper, we analyze the typical DVS circuit, and find that there exists discontinuity of event triggering time. In dim light conditions, the discontinuity becomes prominent. We point out that the discontinuity depends exclusively on the changing speed of light intensity. Experimental results on real event data validate the analysis and the existence of discontinuity that reveals the non-first-order behaviors of DVS in dim light conditions. © 2024, CC BY.

关键词： Pixels

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A Deletable and Modifiable Blockchain Scheme Based on Record Verification Trees and the Multisignature Mechanism

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Computer Modeling in engineering & Sciences 2021年第7期128卷 223-245页

作者： Daojun Han Jinyu Chen Lei Zhang Yatian Shen Yihua Gao Xueheng Wang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng47500China Institute of Data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China Department of Network and Finance Agricultural Bank of China Limited Xuchang BranchXuchang461000China School of Software Pingdingshan UniversityPingdingshan467000China Department of Network and Finance Agricultural Bank of China Limited Nanyang BranchNanyang473000China

As one of the most valuable technologies,blockchains have received extensive attention from researchers and industry circles and are widely applied in various ***,data on a blockchain cannot be *** a result,it is impossible to clean invalid and sensitive data and correct erroneous ***,to a certain extent,hinders the application of blockchains in supply chains and Internet of *** address this problem,this study presents a deletable and modifiable blockchain scheme(DMBlockChain)based on record verification trees(RVTrees)and the multisignature scheme.(1)In this scheme,an RVTree structure is designed and added to the block *** RVTree can not only ensure that a record is true and valid but,owing to its unique binary structure,also verify whether modification and deletion requests are valid.(2)In DMBlockChain,the multisignature mechanism is also *** mechanism requires the stakeholders’signatures for each modification or deletion request and thus ensures that a record will not be modified arbitrarily.A user’s request is deemed valid only if it is dually verified by the RVTree and the multisignature *** analysis finds that DMBlockChain can provide a secure and valid means for modifying and deleting records in a block while ensuring the integrity of the block and that DMBlockChain can effectively save space in some scenarios that require frequent records modification.

关键词： Blockchain record verification trees multisignature DMBlockChain

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Gaussian process multi-label classification based on multi-view similarity fusion 22

Gaussian process multi-label classification based on multi-v...

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Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Hui Xiang Shenming Gu School of Information Engineering Zhejiang Ocean University China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University China

ISBN: (纸本)9781450398343

Multi-view multi-label learning serves as a vital framework for learning from objects with diverse representations and rich semantics. The Gaussian process (GP), as an efficient and flexible Bayesian nonparametric model, the GP has effectively solved various tasks in machine learning. However, the Gaussian process has rarely been directly applied to multi-view multi-label learning scenarios. Therefore, in this paper, we propose a new multi-view multi-label learning method (mvml-DSimGP) based on the Gaussian pass latent variable model (GPLVM), which consists of similar non-parametric mapping function learning, rule constraint learning and label learning. Similarity nonparametric study mapping function for multiple-views data analysis, it studied the view within the similarity and latent mapping function between the parameters. On the basis of the similarity of nonparametric function learning, regular constraints are introduced, and put forward multi-label learning. Instances with similar label information are encouraged to have similar output representations in the embedded space, making it sufficient discernment in the embedded space. Numerous experiments on real-world datasets clearly show that mvml-DSimGP has good performance compared to other general multi-view multi-label learning methods.

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Hypernode: Entity Fusion for data Traceability and Link Prediction

Hypernode: Entity Fusion for Data Traceability and Link Pred...

引用

IEEE International Conference on data Mining (ICDM)

作者： Bingbing Dong Zan Zhang Jiao Li Yi Zhu Chenyang Bu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institute of Big Knowledge Science Hefei University of Technology Hefei China School of Information Engineering Yangzhou University Yangzhou China

In the era of big data, fragmented knowledge, multisource heterogeneity, and different representation forms of the same entities in various data sources have posed considerable challenges to entity fusion. How to effectively integrate multisource knowledge for the same entities has provoked vast amounts of attention and research from multiple disciplines. Most existing methods for entity fusion can be categorized into two classes: one is to establish an association between the same entities, and the other is to delete duplicate entities after knowledge fusion and create a new fusion entity. However, in these two classes of methods, the former does not achieve true knowledge fusion and semantic interoperability, while the latter may cause irreversible loss of original information. In this paper, we propose a novel entity fusion scheme: Hypernode. Hypernode fuses the same entity in different data sources into a new entity while retaining the original data. We verify the effectiveness of Hypernode on multiple models of link prediction experiments. Several practical application cases illustrate the applicability of Hypernode in data traceability, open domain knowledge fusion, and multi-modal knowledge graph fusion.

关键词： Fuses Soft sensors Semantics Predictive models big data Cognition data mining

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