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Journal of Network Intelligence 2022年第4期7卷 1047-1065页

作者： Tang, Zheng-Yi Liu, Chuan Lian, Zhi-Zhu Wang, Jin-Shui Hossain, Md. Alamgir School of Computer Science and Mathematics Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Xiyuangong Road Fujian Province Fuzhou City350121 China Key Laboratory of Hunan Province for Mobile Business Intelligence Hunan University of Technology and Business Yuelu Road Hunan Province Changsha City410205 China Department of Management Hajee Mohammad Danesh Science and Technology University Dinajpur5200 Bangladesh

Service computing techniques have achieved remarkable results in solving the cohesiveness problem of Web applications and are therefore introduced into the IoT do-main to break the trend of inlining IoT systems. However, compared with the traditional Internet, IoT has richer elements and semantic connotations, so the existing service description framework needs to be improved and extended. In this paper, we propose an ontology description framework for IoT services, design corresponding inference rules, and give a storage method for IoT services by integrating the knowledge graph technology for the strongly associated characteristics of IoT services. On this basis, an algorithm for combining IoT services based on graph planning is proposed. The experimental results show that the proposed method in this paper can provide richer entity semantics and ef-fectively improve the efficiency and success rate of IoT service composition. © The Authors.

关键词： Ontology

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Multi-dimensional Graph Linear Canonical Transform

arXiv

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arXiv 2024年

作者： Li, Na Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China

Many multi-dimensional (M-D) graph signals appear in the real world, such as digital images, sensor network measurements and temperature records from weather observation stations. It is a key challenge to design a transform method for processing these graph M-D signals in the linear canonical transform domain. This paper proposes the two-dimensional graph linear canonical transform based on the central discrete dilated Hermite function (2-D CDDHFs-GLCT) and the two-dimensional graph linear canonical transform based on chirp multiplication-chirp convolution-chirp multiplication decomposition (2-D CM-CC-CM-GLCT). Then, extending 2-D CDDHFs-GLCT and 2-D CM-CC-CM-GLCT to M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT. In terms of the computational complexity, additivity and reversibility, M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT are compared. Theoretical analysis shows that the computational complexity of M-D CM-CC-CM-GLCT algorithm is obviously reduced. Simulation results indicate that M-D CM-CC-CM-GLCT achieves comparable additivity to M-D CDDHFs-GLCT, while M-D CM-CC-CM-GLCT exhibits better reversibility. Finally, M-D GLCT is applied to data compression to show its application advantages. The experimental results reflect the superiority of M-D GLCT in the algorithm design and implementation of data compression. Copyright © 2024, The Authors. All rights reserved.

关键词： Chirp modulation

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Hand Detection Based on YOLOv3 6th

Hand Detection Based on YOLOv3

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6th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2019

作者： Yu, Xipu Ma, Linjuan Zhang, Fuquan International Curriculum Center The High School Affiliated To Renmin University of China Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China

ISBN: (纸本)9789811650352

Hand detection is paid more attention to in the field of augmented reality, which can enhance user experience of human–computer interaction. It is more and more popular to use deep neural networks to research hand detection. A new method of hand detection based on YOLOv3 Model is proposed in our work. Our target is to contribute to a real-time hand detection system to make it more accuracy and convenient for the experience of the user and can apply in all aspects of our life. 11 K hands dataset is used to perfrom the experiments and the result is sound. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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E-MAEL: Efficient Multi-Cloud Data Auditing With Error Localization for IoT Consumer Devices in Metaverse

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IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Mingxi Fan, Kuan Lu, Ning Shi, Wenbo Alenazi, Mohammed J.F Chen, Chien-Ming Kumari, Saru Northeastern University at Qinhuangdao College of Computer Science and Engineering Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Shenyang China Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao China Northeastern University College of Computer Science and Engineering Shenyang China Northeastern University at Qinhuangdao sHebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao China Department of Computer Engineering Riyadh11451 Saudi Arabia Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut India

By integrating multi-cloud (MC) storage with the "edge-to-cloud" architecture of consumer IoT, the metaverse paradigm achieves enhanced efficiency in real-time data collection and interactive virtual feedback. However, data stored across multiple clouds is vulnerable to tampering and theft due to the inherent insecurity of cloud computing, and cloud service providers, driven by economic interests, may conceal data losses. Existing data integrity auditing techniques fail to ensure prompt and reliable verification for distributed data in the MC-enabled metaverse paradigm, limiting their effectiveness in eliminating erroneous data swiftly. To address these challenges, we propose E-MAEL (Efficient Multi-cloud Data Auditing with Error Localization) for IoT devices in the metaverse. We design an MC self-auditing framework based on blockchain to eliminate audit delays caused by third-party auditors. A variable-length auditing protocol based on Schnorr is introduced, reducing computation overhead by up to 21% compared to schemes relying on bilinear pairing and RSA assumptions. We develop a single-round error localization strategy based on symmetric unbalanced block design, enabling efficient identification and removal of erroneous data while resisting collusion attacks. A dynamic reputation update strategy using convex functions is proposed, providing robust reputation management for MCs and effectively penalizing audit errors. Theoretical security analyses and experimental results demonstrate the effectiveness of EMAEL in improving computational efficiency and enhancing reliability in MC auditing systems. © 1975-2011 IEEE.

关键词： Cloud storage

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Graph Chirp Signal and Graph Fractional Vertex-Frequency Energy Distribution

arXiv

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arXiv 2025年

作者： Cui, Manjun Zhang, Zhichao School of Mathematics and Statistics The Center for Applied Mathematics of Jiangsu Province The Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University Haikou571158 China

Graph signal processing (GSP) has emerged as a powerful framework for analyzing data on irregular domains. In recent years, many classical techniques in signal processing (SP) have been successfully extended to GSP. Among them, chirp signals play a crucial role in various SP applications. However, graph chirp signals have not been formally defined despite their importance. Here, we define graph chirp signals and establish a comprehensive theoretical framework for their analysis. We propose the graph fractional vertex-frequency energy distribution (GFED), which provides a powerful tool for processing and analyzing graph chirp signals. We introduce the general fractional graph distribution (GFGD), a generalized vertex-frequency distribution, and the reduced interference GFED, which can suppress cross-term interference and enhance signal clarity. Furthermore, we propose a novel method for detecting graph signals through GFED domain filtering, facilitating robust detection and analysis of graph chirp signals in noisy environments. Moreover, this method can be applied to real-world data for denoising more effective than some state-of-the-arts, further demonstrating its practical significance. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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Robust Video Object Segmentation with Restricted Attention

Robust Video Object Segmentation with Restricted Attention

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Huaizheng Zhang Pinxue Guo Zhongwen Le Wenqiang Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China

This paper focuses on the two problems of the similar objects distraction and the lack of robustness for unseen object categories in semi-supervised video object segmentation task. Existing methods have achieved great results on the benchmark dataset, but these two problems still have not been completely solved. We propose the Robust Video Object Segmentation With Restricted Attention (RVOSR), which can suppress the effects caused by similar objects and filter out noise confusion from other irrelevant regions. Meanwhile augmenting the semantic information of features, which makes the features more suitable for video object segmentation task. Extensive experiments demonstrate the effectiveness of our approach and achieve the state-of-the-art performance on the widely-used VOS benchmarks including DAVIS-2016 (92.1% $\mathcal{J}{{\& }}\mathcal{F}$), DAVIS-2017 (86.8% $\mathcal{J}{{\& }}\mathcal{F}$) and YouTubeVOS-2019 (84.8%).

关键词： Semantics Object segmentation Benchmark testing Signal processing information filters Robustness Acoustics

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Multimodal Knowledge Graph Embeddings via Lorentz-based Contrastive Learning

Multimodal Knowledge Graph Embeddings via Lorentz-based Cont...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ruizhou Liu Zongsheng Cao Zhe Wu Qianqian Xu Qingming Huang School of Computer Science and Technology University of Chinese Academy Sciences Beijing China Department of Networked Intelligence Pengcheng Laboratory Shenzhen China Institute of Information Engineering Chinese Academy Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Multimodal knowledge graph embeddings (MKGE) have recently garnered significant attention. Unlike traditional unimodal knowledge graph embeddings, MKGE integrates both structural and multimodal knowledge to represent entities within a unified framework. However, real-world entities exhibit heterogeneity, often resulting in semantic inconsistencies where structurally similar embeddings may diverge significantly in their multimodal representations. Previous approaches primarily focus on directly fusing structural and multimodal embeddings, thus overlooking the issue of semantic-embedding inconsistency. To tackle this issue, we propose a new multimodal knowledge graph embedding method via Lorentz-based contrastive learning (LCKGE). we firstly introduce a well-designed nearest-neighbor fusion module via contrastive learning for multimodal fusion. Then, an attention-based Lorentz transformation is proposed for capturing more complex geometric information in MKGs. Furthermore, a series of comprehensive experiments are conducted to demonstrate the effectiveness of our model. We provide the code and appendix of LCKGE in https://***/RuizhouLiu/LCKGE

关键词： Codes Semantics Knowledge graphs Contrastive learning Benchmark testing

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Anisotropic electrical properties of aligned PtSe_(2) nanoribbon arrays grown by a pre-patterned selective selenization process

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Nano Research 2022年第5期15卷 4668-4676页

作者： Huaipeng Wang Zhifang Liu Yilin Sun Xiaofan Ping Jianlong Xu Yingtao Ding Haowen Hu Dan Xie Tianling Ren School of Integrated Circuits Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China Department of Chemistry Tsinghua UniversityBeijing 100084China School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China Institute of Functional Nano&Soft Materials(FUNSOM) Jiangsu Key Laboratory for Carbon-Based Functional Materials&DevicesSoochow UniversitySuzhou 215123China State Key Lab of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China

This study proposes a feasible and scalable production strategy to naturally obtain aligned platinum diselenide(PtSe_(2))nanoribbon arrays with anisotropic *** anisotropic properties of two-dimensional(2D)materials,especially transition-metal dichalcogenides(TMDs),have attracted great interest in *** dependence of physical properties on their lattice orientations is of particular interest because of its potential in diverse applications,such as nanoelectronics and ***-dimensional(1D)nanostructures facilitate many feasible production strategies for shaping 2D materials into unidirectional 1D nanostructures,providing methods to investigate the anisotropic properties of 2D materials based on their lattice orientations and *** natural alignment of zigzag(ZZ)PtSe_(2) nanoribbons is experimentally demonstrated using angle-resolved polarized Raman spectroscopy(ARPRS),and the selective growth mechanism is further theoretically revealed by comparing edges and edge energies of different orientations using the density functional theory(DFT).Back-gate field-effect transistors(FETs)are also constructed of unidirectional PtSe_(2) nanoribbons to investigate their anisotropic electrical properties,which align with the results of the projected density of states(DOS)*** work provides new insight into the anisotropic properties of 2D materials and a feasible investigation strategy from experimental and theoretical perspectives.

关键词： PtSe_(2) scalable production anisotropic properties nanoribbons density functional theory

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Learning to Deblur Polarized Images

arXiv

引用

arXiv 2024年

作者： Zhou, Chu Teng, Minggui Zhou, Xinyu Xu, Chao Sato, Imari Shi, Boxin National Institute of Informatics Japan National Engineering Research Center of Visual Technology School of Computer Science Peking University China State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Key Laboratory of General AI School of Intelligence Science and Technology Peking University China

A polarization camera can capture four linear polarized images with different polarizer angles in a single shot, which is useful in polarization-based vision applications since the degree of linear polarization (DoLP) and the angle of linear polarization (AoLP) can be directly computed from the captured polarized images. However, since the on-chip micro-polarizers block part of the light so that the sensor often requires a longer exposure time, the captured polarized images are prone to motion blur caused by camera shakes, leading to noticeable degradation in the computed DoLP and AoLP. Deblurring methods for conventional images often show degraded performance when handling the polarized images since they only focus on deblurring without considering the polarization constraints. In this paper, we propose a polarized image deblurring pipeline to solve the problem in a polarization-aware manner by adopting a divide-and-conquer strategy to explicitly decompose the problem into two less ill-posed sub-problems, and design a two-stage neural network to handle the two sub-problems respectively. Experimental results show that our method achieves state-of-the-art performance on both synthetic and real-world images, and can improve the performance of polarization-based vision applications such as image dehazing and reflection removal. Copyright © 2024, The Authors. All rights reserved.

关键词： Copyrights

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Concrete Structural Crack Damage Classification Using Nonlinear Dimension Reduction and Broad Learning System

Concrete Structural Crack Damage Classification Using Nonlin...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Bingshu Wang Jia Lin Xiaodong Zhuang Guanghui Zhang C.L. Philip Chen School of Software Northwestern Polytechnical University Xi'an China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Concrete structural crack damage classification is of importance for road safety. This paper proposes a new method based on broad neural network for crack damage classification in concrete structures. It includes three stages. Firstly, a pre-trained deep neural network is used to extract the features from crack images. Secondly, principal component analysis is used to project the retrieved features from high dimensions to low dimensions. Thirdly, broad learning system is employed to predict the classification using the low-dimensional features. Experimental results demonstrate that this method reduces the model's training time and improves classification accuracy.

关键词： Training Learning systems Dimensionality reduction Accuracy Artificial neural networks Feature extraction Road safety Cybernetics Principal component analysis Image classification

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