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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wudi Chen Chao Zhang Cheng Han Yanjie Ma Yongqing Cai School of Computer Science and Technology Changchun University of Science and Technology China National & Local Joint Engineering Research Center for Special Film Technology and Equipment Changchun China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Neural radiance fields (NeRF) have been successfully applied to many visual tasks. Due to the limitations of traditional style transfer methods in the 3D domain, style transfer methods based on NeRF have emerged. However, existing methods fail to generate stylized images with clear scene textures and high cross-view consistency. Therefore, we design STTCNeRF, a novel method for 3D scene style transfer. We propose a texture consistency loss based on our framework's Merge Net to capture the source textures and inherent consistency, achieving clear and consistent stylized effects. Meanwhile, we propose comprehensive latent style codes as conditional features, remapping them through our proposed Style Net to obtain style vectors conforming to the distributions of 2D stylized images. We utilize the Merge Net to render stylized images that are both reasonable and natural. Extensive experimental results show that STTCNeRF outperforms existing methods in terms of visual perception, image quality and cross-view consistency.

关键词： Image quality Three-dimensional displays Codes Design methodology Gaussian distribution Neural radiance field Visual effects

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Empowering Donors: How Blockchain technology Can Help Ensure Their Contributions Reach the Right Recipients 2

Empowering Donors: How Blockchain Technology Can Help Ensure...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Ajmal, Sahar Muzammil, Muteeb Bin Shoaib, Muhammad Mehmood, Muhammad Hassaan National University of Computer and Emmerging Sciences Department of Software Engineering Faisalabad Pakistan Riphah International University College of Computing Faisalabad Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential. © 2023 IEEE.

关键词： Transparency

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research and implementation of a planimetric measurement technology based on machine vision

Research and implementation of a planimetric measurement tec...

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2022 International Conference on Signal Processing, computer Networks, and Communications, SPCNC 2022

作者： Zhou, Qingsen Zhao, Laiding Bai, Xiaodong College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications Jiangsu Nanjing210003 China National Local Joint Engineering Research Center for Communication and Network Technology Nanjing University of Posts and Telecommunications Jiangsu Nanjing210003 China School of Computer Science and Technology Hainan University Haikou570228 China

ISBN: (纸本)9781510664616

To solve the issues with industrial traditional area measurement instruments equipped with infrared sensors, which are bulky and compute slowly, a planimetric measuring method based on machine vision is developed. The area of the plane is calculated through three processes: 3-D reconstruction by monocular camera calibration, top view by perspective transformation and image segmentation to obtain the proportion of the object to be measured. This paper enhances the calculation of the world coordinate points in the 3-D reconstruction process and lowers the measurement error from more than 1% to 0.28%. It solves the drawbacks of the conventional measuring device, which is huge and slow, and can correctly measure the planar area with less expensive hardware, which has certain application value. © 2023 SPIE.

关键词： Image segmentation

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Language-guided Image Reflection Separation

Language-guided Image Reflection Separation

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Haofeng Zhong Yuchen Hong Shuchen Weng Jinxiu Liang Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University AI Innovation Center School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper studies the problem of language-guided re-flection separation, which aims at addressing the ill-posed reflection separation problem by introducing language de-scriptions to provide layer content. We propose a unified framework to solve this problem, which leverages the cross-attention mechanism with contrastive learning strategies to construct the correspondence between language descriptions and image layers. A gated network design and a ran-domized training strategy are employed to tackle the rec-ognizable layer ambiguity. The effectiveness of the pro-posed method is validated by the significant performance advantage over existing reflection separation methods on both quantitative and qualitative comparisons.

关键词： Training computer vision Annotations Natural languages Contrastive learning Logic gates Reflection

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Hybrid of Spans and Table-Filling for Aspect-Level Sentiment Triplet Extraction 30

Hybrid of Spans and Table-Filling for Aspect-Level Sentiment...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Nuo, Minghua Guo, Chaofan College of Computer Science Inner Mongolia University Inner Mongolia Hohhot010021 China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Hohhot010021 China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Inner Mongolia Hohhot010021 China

ISBN: (纸本)9782493814104

Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research. Recently, researchers have proposed different tagging schemes, containing tagging of words, tagging of word-pairs, and tagging of spans. However, the first two of these methods are often insufficient for the identification of multi-word terms, while the span tagging can label the entire phrase span, but it lacks the interactive information between words. In this paper, we propose Hybrid of Spans and Table-filling (S&T) model which combining span with table-filling. Specifically, S&T model achieve full fusion of syntactic and contextual features through cross-attention and generate the structures of word-pair table through Biaffine. Then, our model converts it to a span table by computing semantic distance based on syntactic dependency tree, which can enrich each unit of span table with semantic and interactive information. Meanwhile, the initial sentence features are constructed as simple phrase tables to enhance textual information of the phrase itself. In decoding, we define 8 types of labels for identifying three dimensions including aspect, opinion, and sentiment. Finally, the extensive experiments on widely-used dataset show S&T model achieves competitive results in ASTE task, the results certify the effectiveness and robustness of our S&T model. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Filling

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Unified regularity measures for sample-wise learning and generalization

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Visual Intelligence 2024年第1期2卷 1-20页

作者： Zhang, Chi Yuan, Meng Ma, Xiaoning Liu, Yu Lu, Haoang Wang, Le Su, Yuanqi Liu, Yuehu Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an 710049 China National KeyLaboratory of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University Xi’an 710049 China National Engineering Research Center for Visual Information and Applications Xi’an Jiaotong University Xi’an 710049 China School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China

Fundamental machine learning theory shows that different samples contribute unequally to both the learning and testing processes. Recent studies on deep neural networks (DNNs) suggest that such sample differences are rooted in the distribution of intrinsic pattern information, namely sample regularity. Motivated by recent discoveries in network memorization and generalization, we propose a pair of sample regularity measures with a formulation-consistent representation for both processes. Specifically, the cumulative binary training/generalizing loss (CBTL/CBGL), the cumulative number of correct classifications of the training/test sample within the training phase, is proposed to quantify the stability in the memorization-generalization process, while forgetting/mal-generalizing events (ForEvents/MgEvents), i.e., the misclassification of previously learned or generalized samples, are utilized to represent the uncertainty of sample regularity with respect to optimization dynamics. The effectiveness and robustness of the proposed approaches for mini-batch stochastic gradient descent (SGD) optimization are validated through sample-wise analyses. Further training/test sample selection applications show that the proposed measures, which share the unified computing procedure, could benefit both tasks. © The Author(s) 2024.

关键词： Forgetting events Memorization-generalization mechanism Network generalization Sample regularity Sample reweighting

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Optimizing Resource Block Allocation for Multicast in Beyond 5G Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Ru-Jun Wang, Chih-Hang Yang, De-Nian Lee, Guang-Siang Chen, Wen-Tsuen Sheu, Jang-Ping National Tsing Hua University Department of Computer Science Hsinchu Taiwan Academia Sinica Institute of Information Science Taiwan National Chung Cheng University Department of Computer Science and Information Engineering Taiwan Academia Sinica Institute of Information Science and the Research Center for Information Technology Innovation Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan

New radio (NR) and non-orthogonal multiple access (NOMA) offer scalable and efficient resource allocation in Beyond 5G (B5G) networks. NR implements mixed numerology with flexible frame structures for future compatibility, whereas NOMA allows users with different channel states to share an identical Physical Resource Block (PRB). Multi-connectivity enables a user to connect to multiple networks for reliability, and multicast conveys data to users simultaneously that request the same content. However, resource allocation in the NOMA-based mixed numerology system with multi-connectivity for multicast remains unexplored. The problem is challenging due to 1) the different shapes of PRBs in NR and 2) the shared locations of PRBs in a frame with NOMA. In this paper, we formulate a new optimization problem, named Multicast, Multi-connectivity, and Multi-Dimensional Resource Allocation Problem (M 3 DRAP), and prove its NP-hardness and inapproximability. We propose an approximation algorithm for general M 3 DRAP with the ideas of Multicast Inter-Numerology Relation, Layer Dissimilarity, Subgrouping Nonuniformity, and Segmentation Preference. To find the intrinsic properties of PRB allocation for multicast in NOMA-based networks, we consider a single B5G usage scenario (e.g., eMBB, URLLC, or mMTC) and propose another approximation algorithm. Simulations demonstrate our algorithms improve the weighted sum rate by over 50% and increase the user satisfaction ratio by 1.5x. © 2025 IEEE.

关键词： 5G mobile communication systems

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Graphene-based dual-function acoustic transducers for machine learning-assisted human-robot interfaces

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InfoMat 2023年第2期5卷 66-79页

作者： Hao Sun Xin Gao Liang-Yan Guo Lu-Qi Tao Zi Hao Guo Yangshi Shao Tianrui Cui Yi Yang Xiong Pu Tian-Ling Ren Institute of Microelectronics and Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijingPeople's Republic of China CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijingPeople's Republic of China State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingPeople's Republic of China School of Computer Science&Technology Beijing Institute of TechnologyBeijingPeople's Republic of China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijingPeople's Republic of China

Human–robot interface(HRI)electronics are critical for realizing robotic ***,we report graphene-based dual-function acoustic transducers for machine learning-assisted human–robot interfaces(GHRI).The GHRI functions both an artificial ear through the triboelectric acoustic sensing mechanism and an artificial mouth through the thermoacoustic sound emission *** success of the integrated device is also attributed to the multifunctional laser-induced graphene,as either triboelectric materials,electrodes,or thermoacoustic *** systematically optimizing the structure parameters,the GHRI achieves high sensitivity(4500 mV Pa^(–1))and operating durability(1000000 cycles and 60 days),capable of recognizing speech identities,emotions,content,and other information in the human *** the assistance of machine learning,30 speech categories are trained by a convolutional neural network,and the accuracy reaches 99.66%and 96.63%in training datasets and test ***,GHRI is used for artificial intelligence communication based on recognized speech *** work shows broad prospects for the development of robotic intelligence.

关键词： human-robot interface laser-induced graphene machine learning thermoacoustic triboelectric nanogenerator

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Blockchain-Based Secure and Efficient Secret Image Sharing With Outsourcing Computation in Wireless Networks

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IEEE Transactions on Wireless Communications 2024年第1期23卷 423-435页

作者： Zhou, Zhili Wan, Yao Cui, Qi Yu, Keping Mumtaz, Shahid Yang, Ching-Nung Guizani, Mohsen Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Department of Applied Informatics Silesian University of Technology Gliwice44-100 Poland Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom Department of Computer Science and Information Engineering National Dong Hwa University Hualien97401 Taiwan Abu Dhabi United Arab Emirates

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing n shadow images in the way that any subset of k shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the (k,n) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks. © 2023 IEEE.

关键词： Smart contract

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Improved Particle Swarm Optimization Algorithm for EIT Image Reconstruction 20

Improved Particle Swarm Optimization Algorithm for EIT Image...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Kahouli, Oumayma Hafsa, Mariem Hellara, Hiba Bennour, Imed Eddine Essoukri Ben Amarax, Najoua Kanoun, Olfa University of Sousse National Engineering School of Sousse Department of Computer Science Sousse4023 Tunisia National Engineering School of Sousse Laboratory of Advanced Technology and Intelligent Systems Tunisia Technische Universität Chemnitz Laboratory of Measurement and Sensor Technology Chemnitz Germany University of Sfax Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Center of Sfax Tunisia

ISBN: (纸本)9798350332568

Electrical Impedance Tomography (EIT) is a clinical imaging technique that gained a lot of attention because it is a non-invasive and radiation-free method. To obtain an inner image of the domain under study, a specific number of electrodes is attached to the surface of the object, then through a pair of electrodes, a low alternative current is injected. Potentials produced by this injection are measured with the remaining electrodes. Based on these boundary voltages measurements, the inner conductivity can be inferred, thus the image is reconstructed. The focus of this paper is to overcome the major weakness of the EIT image reconstruction technique, which is the low spatial resolution of the reconstructed images caused by the non-linearity of EIT inverse problem. This paper considers this problem, an optimization problem that is solved using the Particle Swarm Optimization (PSO) meta-heuristic. A variant of the standard PSO is proposed and validated, first on a circular mesh composed of 686 triangles then on a lung mesh composed of 2707 triangles. © 2023 IEEE.

关键词： Electrodes

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