检索结果-内蒙古大学图书馆

41st computer Graphics International Conference, CGI 2024

作者： Aloui, Sarra Wang, Yu Fan-fan, Zhou College of Computer Science and Software Engineering Hohai University Nanjing China First Affiliated Hospital of Kunming Medical University Kunming China

ISBN: (纸本)9783031818059

There are 466 M deaf across the world according to World Health Organisation. They are deprived of their basic human right which is communicating with others. Many solutions have been developed to help hard-of-hearing people overcome communication barriers. The existing sign language recognition research focuses on sign languages other than Tunisian Sign Language. Consequently, a Tunisian Sign Language dataset, TunSL-D, was developed for this research. The dataset contains static images that are more complex and can generalize the classification tasks. The images represent the most used words and alphabets in Tunisian Sign language. This paper proposes a novel model TunSLR-25 that recognizes and classifies static Tunisian sign language. The model is first trained on the words data, achieving an accuracy of 95.49%, then trained on both words and alphabets, achieving an accuracy of 88%, with a low parameter number of 1,098,901. The TunSL-D has been trained on SLRNet-8 CNN, a 4-layer CNN model, pre-trained models Resnet50, Resnet101, and InceptionV3. The paper compares the performance of these CNN models and the proposed CNN model TunSLR-25. The system will be integrated into a web application designed to be accessible and user-friendly for this community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Classification SLR Tunisian Sign Language

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Authentication Protocol for RFID-Assisted Supply Chain Management System

引用

IEEE Internet of Things Journal 2025年第9期12卷 12845-12852页

作者： Tariq, Tayyaba Kuo, Wen-Chung Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J. F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia

In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system in RFID-based supply chain management, which is intricately linked with the advances in the Internet of Things (IoT). RFID technology employs electromagnetic fields to identify and track tags on objects and revolutionizes the management and tracking of items in the supply chain. However, the public communication among RFID tags, RFID readers, and supply chain infrastructure predominantly escalates security and privacy challenges. Several authentication protocols have been proposed to overcome these challenges. However, the vulnerability of most proposed protocols to numerous security attacks renders them inefficient. Therefore, to address these crucial challenges, we devised an RFID-based authentication protocol for supply chain management systems. The incorporation of a physically unclonable function (PUF) into the protocol fortifies the system against physical tampering attacks. To validate the security and effectiveness of the devised protocol, both informal and formal security analysis are conducted. The formal security analysis is conducted using the widely used random oracle model. The informal security analysis reveals that the devised protocol provides enhanced and efficient security features. Furthermore, we perform a comparative analysis with related protocols, focusing on critical performance metrics like communication cost, computation cost, and overall security features. The results of the comparative analysis are promising, indicating a substantial 30.92% reduction in computational cost and a 23.98% reduction in communication cost in comparison to related protocols, thus highlighting the protocol’s superior performance and resource efficiency. © 2014 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

Deformable Few-Shot Face Cartoonization via Local to Global Translation

引用

Computational Visual Media 2025年第2期11卷 269-287页

作者： Yang Zhou Shengshu Li Hui Huang College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Cartoonizing portrait images is a stylish and eye-catching application in both computer vision and graphics. We aimed to train a face cartoonization model using very few (e.g., 5–10) style examples. The main difficulty in this challenging task lies in producing stylizations of high quality while preserving the identity of the input, particularly when the style examples contain strong exaggerations. To address this, we propose a novel cross-domain center loss for few-shot generative adversarial network (GAN) adaptation, which forces the distribution of the target domain to be similar to that of the source. We then employ it to solve this few-shot problem along with a two-stage strategy. Stage I generates an intermediate cartoonization for the input, where we first stylize the individual facial components locally and then deform them to mimic the desired exaggeration under the guidance of landmarks. Stage II focuses on global refinement of the intermediate image. First, we adapt a pretrained StyleGAN model using the proposed cross-domain center loss to the target domain defined by a few examples. Subsequently, the intermediate cartoonization from Stage I can be holistically refined through GAN inversion. The generative power of StyleGAN guarantees high image quality, while the local translation and landmark-guided deformation applied to facial components provide high identity fidelity. Experiments show that the proposed method outperforms state-of-the-art few-shot stylization approaches both qualitatively and quantitatively.

关键词： Translation Faces Generative adversarial networks Deformation Shape Feature extraction Visualization Neural style transfer Generators Adaptation models

来源：评论

学校读者我要写书评

暂无评论

Uni4DAL: A Unified Baseline for Multi-dataset 4D Auto-Labeling 27th

Uni4DAL: A Unified Baseline for Multi-dataset 4D Auto-Label...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Yang, Zhiyuan Wang, Xuekuan Zhang, Wei Tan, Xiao Lu, Jinchen Wang, Jingdong Ding, Errui Lai, Zhihui Zhao, Cairong Department of Computer Science and Technology Tongji University Shanghai201804 China Baidu Inc. Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783031781124

The 4D auto-labeling system, with its potential to enhance data annotation efficiency for 3D object detection, has garnered significant attention. However, its adoption has been hampered by the high costs associated with temporally annotated long-sequential training data and limited generalization capabilities across diverse scenarios. In this paper, we hypothesize that a multi-dataset approach can address these challenges and, accordingly, first introduce a Unified training pipeline for multi-dataset 4D Auto-Labeling, namely Uni4DAL. We recognize that this is a challenging task, primarily due to data-level variations and feature-level inconsistencies among various datasets. Motivated by this understanding, we initially propose a series of Data-Level Alignment (DLA) operations to mitigate potential discrepancies between diverse datasets and ensure synchronized training progress across samples from multiple datasets. Furthermore, to address feature-level inconsistencies, we introduce the Mixed Expert Models Voxel Feature Encoding (MoE-VFE) module, which aims to extract both domain-specific and domain-generalizable features. Additionally, we employ a Domain-Adaptive Hard Example Mining (DA-HEM) technique to leverage both data-level and feature-level consistencies, ensuring that the model pays enhanced attention to the challenging samples during multi-dataset training. Finally, comprehensive experiments demonstrate that Uni4DAL significantly improves performance on the nuScenes, Argoverse2, and Waymo datasets, and exhibits greater robustness with insufficient training data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 4D auto-labeling Multi-dataset training Offline 3D object detection

来源：评论

学校读者我要写书评

暂无评论

Education and Teaching Research of IoT Embedded Subject under the National Vocational and Technical Skills Standards 6

Education and Teaching Research of IoT Embedded Subject unde...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Yanglu Wang, Xingchen Yadav, Amit Shah, Abhishek Pratap College of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University Darwin Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

This research study analyzes six key factors in the education and teaching of IoT embedded direction: training objectives (which direction to teach), curriculum system (what to teach), teaching organization (how to teach), faculty team (who teaches), and teaching evaluation (how well the teaching is) under the current status of embedded teaching on the Internet of Things (IoT) engineering discipline. Guided by the latest National Occupational Technical Skill Standards for IoT engineering professionals and in accordance with the educational philosophy of Chengdu Neusoft University, here we propose a design for teaching system for IoT embedded talent courses, clarified the key elements and formulated new ideas for the current development of talents in this direction to assist improvement and perfection of related curriculum systems, and to promote the establishment and operation of practical and technological activity platforms. It aims to provide practical references for the construction of professionalism in education and teaching standards of IoT. © 2025 IEEE.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Velocity Field-Based Surveillance Video Frame Deletion Detection Using Siamese Network 27th

Velocity Field-Based Surveillance Video Frame Deletion Detec...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Su, Yang Tan, ShunQuan Huang, Jiwu College of Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783031783111

Surveillance videos play a crucial role in providing evidences. However, the deletion of even a few frames can significantly impact the interpretation of events, while the deletion can be performed easily using video editing softwares without leaving visual traces. This paper introduces a novel method to detect video frame deletion based on velocity field characteristics. The main idea is to convert long videos into a feature sequence, which appears as a sequence containing outlier values for videos with frame deletions, and as a sequence without outlier values for original videos. The proposed approach employs a siamese network to distinguish frame deletions between adjacent frames. A video can be transformed into a feature sequence through the siamese network, and the resulting sequence is fed into a binary classifier for final classification. Experimental results demonstrate the effectiveness of our proposed approach, even for longer videos (e.g., 2000 frames) with minimal frame deletions (e.g., 1 frame). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital forensics

来源：评论

学校读者我要写书评

暂无评论

Adaptive Local Neighborhood Window Laplacian Eigenmaps 24

Adaptive Local Neighborhood Window Laplacian Eigenmaps

引用

13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Xu, Jiayi Huang, Qirui Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400717482

Manifold learning method is a dimensionality reduction method that treats the data in non-Euclidean space as a Euclidean space in a local scope. However, most existing manifold learning methods cannot obtain the true neighborbood relationship of each sample point well. Therefore, we propose a manifold learning method based on adaptive local neighborhood. This method can adaptively select a neighborhood window. It can be approximated as a local Euclidean space based on the sample characteristics. To implement this method, a specific manifold learning method, named Laplacian Eigenmap of Adaptive Local Neighborhood Window (ALNWLE) is proposed. ALNWLE mainly contains two modules, namely the manifold structure learning module (MSLM) and the manifold structure pattern recognition module (MSPR). MSLM constructs an adaptive local neighborhood adjacency matrix to constrain the learning of the manifold structure. MSPR uses the interpolation method to recognize new sample patterns. The adaptive local neighborhood method proposed in this paper is a general method which is applicable to any manifold learning method. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

DKGV: A Dynamic Knowledge Graph Visualization Method Based on Force-Directed Layout 19

DKGV: A Dynamic Knowledge Graph Visualization Method Based o...

引用

19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry, VRCAI 2024

作者： Zhou, Hong Huang, Sunjie Zheng, Yushan Liu, Jinyan Chen, Yang Chen, Xiangyuan Li, Jun College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Academy of Inspection and Quarantine Shenzhen China Shenzhen University Shenzhen China

ISBN: (纸本)9798400713484

The dynamic knowledge graph is a data structure that adds temporal information to the nodes and edges of a traditional knowledge graph. It describes the changing processes of entities and relationships over time, thereby enriching and updating the expression of knowledge. The dynamic knowledge graph possesses characteristics such as time and labels;it is not only a knowledge graph but also a type of dynamic graph containing heterogeneous information. To address the issue that traditional dynamic graph visualization methods do not account for heterogeneous information in the layout, leading to a relatively random distribution of node labels in the visualization results, this paper proposes DKGV, a dynamic knowledge graph visualization method based on a force-directed layout. DKGV utilizes an improved GCN to generate the initial layout of the dynamic knowledge graph data, performs static force-directed layout iterative processing, and conducts dynamic force-directed layout calculations on the dynamic knowledge graph. Finally, it employs a force-directed layout with boundaries to support three-dimensional temporal display after view transformation. Experimental results show that DKGV can maintain the stability of the node layout during the evolution of the dynamic knowledge graph while making the overall layout more regular to better display the relationships between entities. © 2024 ACM.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Robust copy-move detection and localization of digital audio based CFCC feature

引用

Multimedia Tools and Applications 2025年第11期84卷 9573-9589页

作者： Wang, Dongyu Li, Xiaojie Shi, Canghong Niu, Xianhua Xiong, Ling Wu, Hanzhou Qian, Qing Qi, Chao School of Computer and Software Engineering Xihua University Chengdu610039 China The College of Computer Science Chengdu University of Information Technology Chengdu610225 China School of Information Shanghai University Shanghai200444 China School of Information Guizhou University of Finance and Economics Guiyang550000 China

Copy-move forgery is a common audio tampering technique in which users copy the contents of one speech and paste them into another region of the same speech signal, thus achieving the effect of tampering with the semantics. To verify the authenticity of the audio, this paper proposes a method to detect and localize audio copy-move forgery based on the cochlear filter of cochlear filter cepstral coefficients (CFCC) feature. The pitch tracking algorithm is used to distinguish the voiced and unvoiced segments in the audio, and then the CFCC coefficients are extracted for each voiced segment. The CFCC feature simulates the entire transmission process of signals in the cochlear basilar membrane using wavelet transformation. Finally, we use Pearson correlation coefficients (PCCs) and dynamic time warping (DTW) in combination to compare the similarity of voiced segments, accurately determining the tampered locations in the audio through threshold judgment. Through extensive experiments on relevant datasets, this algorithm achieves a precision rate of 98.39% and a recall rate of 98.00% in detecting audio without post-processing. Even when detecting audio that has undergone different post-processing, the precision and recall rates remain above 90%. Compared to existing methods, this approach not only achieves precise localization of replicated segments but also demonstrates superior experimental results. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

引用

IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：