检索结果-内蒙古大学图书馆

Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

Digital Communications and Networks 2025年第2期11卷 482-492页

作者： Puning Zhang Lei Tan Zhigang Yang Fengyi Huang Lijun Sun Haiying Peng School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing Innovation Center of Industrial Big-Data Co.Lt

The lack of facial features caused by wearing masks degrades the performance of facial recognition systems. Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer. Besides, previous research fails to consider the facial characteristics including occluded and unoccluded parts. To solve the above problems, we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion. Specifically, the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition. Then, a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial. Furthermore, a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load, computational power, bandwidth, and delay tolerance constraints of the edge. This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy. The experimental results show that the proposed method achieves an average gain of about 21% in recognition latency, while the accuracy of the face recognition task is basically the same compared to the baseline method.

关键词： Occluded face recognition Cross-domain feature fusion Device-edge collaboration

来源：评论

学校读者我要写书评

暂无评论

Edge-Cloud Cooperation-Driven intelligent Sustainability Evaluation Strategy Based on IoT and CPS for Energy-Intensive Manufacturing Industries

引用

IEEE Internet of Things Journal 2025年第9期12卷 12287-12297页

作者： Ma, Shuaiyin Huang, Yuming Chen, Yanping Xiao, Qinge Xu, Jun Leng, Jiewu Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing School of Computer Science and Technology Xi’an710121 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Xidian University Advanced Manufacturing Technology Innovation Center Guangzhou Institute of Technology Guangzhou510555 China Guangdong University of Technology Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangzhou510006 China

The advancement of the Industry 5.0 in information technology has led to increased interest in integrating edge-cloud cooperation with Internet of Things (IoT) and cyber-physical system (CPS) designs. This integration effectively reduces service delays and provides real-time analysis feedback to physical spaces, attracting attention from both academia and industry. These advanced technologies enhance production system intelligence, their alignment with circular economy principles for promoting sustainability has been overlooked. To address this gap, this article proposes an intelligent sustainability evaluation strategy driven by edge-cloud cooperation, IoT, and CPS. The proposed approach aims to enhance production sustainability and intelligence through circular economy perspectives. It introduces improved gray relation analysis and deep clustering network techniques to extract meaningful insights from diverse indicators within the evaluation system. By analyzing relationships between different equipment and workshops, it provides an analytical method that enhances production efficiency while reducing energy consumption and resource waste. To further validate the proposed method, an illustrative example using a partner company’s production data demonstrates its accuracy. © 2014 IEEE.

关键词： Circular economy

来源：评论

学校读者我要写书评

暂无评论

DMC-Watermark: A backdoor richer watermark for dual identity verification by dynamic mask covering

引用

Applied Intelligence 2025年第8期55卷 1-15页

作者： Zhu, Yujia Wang, Ruoxi Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Key Laboratory of Advanced Computing Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

With the increasing use of neural networks, the importance of copyright protection for these models has gained significant attention. Backdoor watermarking is one of the key methods for protecting copyright. However, on the one hand, most existing backdoor watermarks are triggered by visual images, making them easily detectable, and therefore vulnerable to various attacks. On the other hand, it is difficult for these methods to carry information related to the creator’s identity which can easily lead to fraudulent claims of ownership. These factors contribute to the vulnerability and limitations of backdoor watermarking. In this paper, we propose DMC-Watermark, a backdoor richer watermarking method that uses dynamic mask-covered image structures as triggers. Leveraging the semantic preservation of image structure in transformation attacks, we select image structure as triggers. Furthermore, we convert the author-related information into an array of color information and apply it as a mask to the extracted image structures, allowing it to serve as a second layer of verification during the validation phase to resist fraudulent claims of ownership. The final trigger pattern, embedded with author-related image structures, is applied to the selected images in the trigger set, generating a final trigger set that is trained together with clean samples to produce a protected model. The experiments show that the proposed DMC-Watermark performs well in terms of fidelity, invisibility, undetectability, functionality, dual verification and robustness on three different datasets and four representative DNNs, and it has wide applicability and excellent results in high-resolution images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image watermarking

来源：评论

学校读者我要写书评

暂无评论

T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

引用

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

来源：评论

学校读者我要写书评

暂无评论

Towards Adaptive Masked Structural Learning for Graph-Level Clustering

引用

IEEE Transactions on Network Science and Engineering 2025年第3期12卷 2021-2032页

作者： Yang, Jinbin Cai, Jinyu Zhang, Yunhe Huang, Sujia Wang, Shiping Fuzhou University College of Computer and Data Science Fuzhou350116 China Bank of Quanzhou Company Ltd. Quanzhou362046 China National University of Singapore Institute of Data Science 117602 Singapore Shenzhen Research Institute of Big Data Shenzhen518172 China Chinese University of Hong Kong Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen518172 China

Graph data presents a vast landscape for real-world applications. Current graph-level clustering approaches predominantly utilize graph neural networks to capture the intricate structural information for graph data. However, a significant challenge arises in effectively integrate structural and feature information under the prevalent noise in the real-world scenario. The advent of masking strategies has marked significant strides in boosting model robustness, accommodating incomplete data, and enhancing generalization capabilities. Yet, research attention on leveraging mask strategy for facilitating graph-level clustering is still limited. In this paper, we introduce a novel graph-level clustering method, towards adaptive masked structural learning for graph-level clustering. The method performs adaptive masking through reconstruction loss, and jointly adaptive mask representation learning and clustering in an end-to-end unsupervised framework. The mutual information between maximized the entire graph and substructure representations is also utilized to learn to generate cluster-oriented graph-level representations. Extensive experiments on eight real graph-level benchmark datasets demonstrate the effectiveness and superiority of the proposed method. © 2013 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

JDC-GCN: joint diversity and consistency graph convolutional network

引用

Neural computing and Applications 2025年 1-17页

作者： Lin, Renjie Yao, Jie Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

In real-world scenarios, multi-view data comprises heterogeneous features, with each feature corresponding to a specific view. The objective of multi-view semi-supervised classification is to enhance classification performance by leveraging the inherent complementary and consistent information present within diverse views. Nevertheless, many existing frameworks primarily focus on assigning suitable weights to different views while neglecting the importance of consistent information. In this paper, a multi-view semi-supervised classification framework called joint diversity and consistency graph convolutional network (JDC-GCN) is proposed. Firstly, the structure of graph convolutional network is introduced to the multi-view semi-supervised classification, capable of propagating the label information over the topological structure of multi-view data. Secondly, the proposed JDC-GCN captures the complementary and consistent information from multiple views through two indispensable sub-modules, Diversity-GCN and Consistency-GCN, respectively. Finally, the attention mechanism is leveraged to dynamically adjust the weights of various views, allowing us to measure the significance of heterogeneous features and the consistent graph without introducing additional parameters. Comprehensive experiments on eight multi-view datasets are conducted to validate the effectiveness of the JDC-GCN algorithm. The results show that the proposed method exhibits superior classification performance compared to other state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Retrieval-Augmented Fine-Tuning for Improving Retrieve-and-Edit Based Assertion Generation

引用

IEEE Transactions on Software Engineering 2025年第5期51卷 1591-1614页

作者： Li, Hongyan Sun, Weifeng Yan, Meng Xu, Ling Li, Qiang Zhang, Xiaohong Zhang, Hongyu Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

Unit Testing is crucial in software development and maintenance, aiming to verify that the implemented functionality is consistent with the expected functionality. A unit test is composed of two parts: a test prefix, which drives the unit under test to a particular state, and a test assertion, which determines what the expected behavior is under that state. To reduce the effort of conducting unit tests manually, Yu et al. proposed an integrated approach (integration for short), combining information retrieval with a deep learning-based approach to generate assertions for test prefixes, and obtained promising results. In our previous work, we found that the overall performance of integration is mainly due to its success in retrieving assertions. Moreover, integration is limited to specific types of edit operations and struggles to understand the semantic differences between the retrieved focal-test (focal-test includes a test prefix and a unit under test) and the input focal-test. Based on these insights, we then proposed a retrieve-and-edit approach named EDITAS to learn the assertion edit patterns to improve the effectiveness of assertion generation in our prior study. Despite being promising, we find that the effectiveness of EDITAS can be further improved. Our analysis shows that: ① The editing ability of EDITAS still has ample room for improvement. Its performance degrades as the edit distance between the retrieval assertion and ground truth increases. Specifically, the average accuracy of EDITAS is 12.38% when the edit distance is greater than 5. ② EDITAS lacks a fine-grained semantic understanding of both the retrieved focal-test and the input focal-test themselves, which leads to many inaccurate token modifications. In particular, an average of 25.57% of the incorrectly generated assertions that need to be modified are not modified, and an average of 6.45% of the assertions that match the ground truth are still modified. Thanks to pre-trained models employing

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

Rational molecular design of P-doped porous carbon material for the VOCs adsorption

引用

Chinese Journal of Chemical Engineering 2025年第3期79卷 155-163页

作者： Changqing Su Wentao Jiang Yang Guo Guodong Yi Zengxing Li Huan Li Hunan Key Laboratory of Carbon Neutrality and Intelligent Energy School of Resources & Environment Hunan University of Technology and Business Xiangjiang Laboratory School of Advanced Interdisciplinary Studies Research Institute for Big Data & Internet Innovation Hunan University of Technology and Business Industry Training Center Shenzhen Polytechnic University

The objective of this study was to identify and synthesize functional groups for the efficient adsorption of volatile organic compounds（VOCs） through a combination of theoretical calculations,molecular design,and experimental *** density functional theory（DFT） calculation,focusing on the P-containing functional groups,showed that methanol adsorption was dominated by the electrostatic interaction between the carbon surface and methanol,while toluene was mainly trapped through π-π dispersive interaction between toluene molecule and functional group *** experimental results showed the phosphorus-doped carbon materials（PCAC） prepared by directly activating potassium phytate had a phosphorus content of up to 4.5%（atom）,mainly in the form of C—O—P（O）(OH)*** material exhibited a high specific area(987.6 m2·g-1) and a large adsorption capacity for methanol(440.0 mg·g-1) and toluene(350.1 mg·g-1).These properties were superior to those of the specific commercial activated carbon（CAC）sample used for comparison in this *** adsorption efficiencies per unit specific surface area of PCAC were 0.45 mg·g-1m2for methanol and 0.35 mg·g-1·m-2for *** study provided a novel theoretical and experimental framework for the molecular design of polarized elements to enhance the adsorption of polar gases,offering significant advancements over existing commercial solutions.

关键词： Adsorption Density functional theory Doped porous carbon Molecular design VOCs

来源：评论

学校读者我要写书评

暂无评论

Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第1期6卷 144-158页

作者： Zhang, Dengyong He, Ruiyi Liao, Xin Li, Feng Chen, Jiaxin Yang, Gaobo Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation the School of Computer and Communication Engineering Changsha410114 China Hunan University Changsha410082 China

Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works. © 2024 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Weak Multi-Label data Stream Classification Under Distribution Changes in Labels

引用

IEEE Transactions on big data 2025年第3期11卷 1369-1380页

作者： Zou, Yizhang Hu, Xuegang Li, Peipei Hu, Jun Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei230002 China National University of Singapore School of Computing 119077 Singapore

Multi-label stream classification aims to address the challenge of dynamically assigning multiple labels to sequentially arrived instances. In real situations, only partial labels of instances can be observed due to the expensive human annotations, and the problem of label distribution changes arises from multiple labels in a streaming mode, but few existing works jointly consider such challenges. Motivated by this, we propose the problem of weak multi-label stream classification (WMSC) and an online classification algorithm robust to weak labels. Specifically, we incrementally update the margin-based model using information from both the past model and the current incoming instance with partially observed labels. To increase the robustness to weak labels, we first adjust the classification margin of negative labels using the label causality matrix, which is constructed by the conditional probability of label pairs. Second, we introduce the label prototype matrix to regulate the margin by controlling the weighting parameter of the slack term. Additionally, to handle the potential distribution changes in labels, we utilize the instance-specific threshold via online thresholding to perform binary classification, which is formulated as a regression problem. Finally, theoretical analysis and empirical experimental results are presented to demonstrate the effectiveness of WMSC in classifying unobserved streaming instances. © 2015 IEEE.

关键词： Labeled data

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：