检索结果-内蒙古大学图书馆

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Huilong Zhang, Yingxue Shi, Lei Zhang, Shuang Kou, Feifei Yang, Jiapeng Zhu, Chuangying Luo, Jia College of Engineering Hebei Normal University China College of Computer and Cyber Security Hebei Normal University China State Key Laboratory of Media Convergence and Communication Communication University of China China Key Laboratory of Education Informatization for Nationalities Yunnan Normal University Ministry of Education China School of Computer Science Beijing University of Posts and Telecommunications China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology China Chongqing Research Institute Beijing University of Technology China

Due to its low storage cost and fast search speed, cross-modal retrieval based on hashing has attracted widespread attention and is widely used in real-world applications of social media search. However, most existing hashing methods are often limited by uncomprehensive feature representations and semantic associations, which greatly restricts their performance and applicability in practical applications. To deal with this challenge, in this paper, we propose an end-to-end graph attention network hashing (EGATH) for cross-modal retrieval, which can not only capture direct semantic associations between images and texts but also match semantic content between different modalities. We adopt the contrastive language image pretraining (CLIP) combined with the Transformer to improve understanding and generalization ability in semantic consistency across different data modalities. The classifier based on graph attention network is applied to obtain predicted labels to enhance cross-modal feature representation. We construct hash codes using an optimization strategy and loss function to preserve the semantic information and compactness of the hash code. Comprehensive experiments on the NUS-WIDE, MIRFlickr25K, and MS-COCO benchmark datasets show that our EGATH significantly outperforms against several state-of-the-art methods. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Emergency Resource Dispatch Based on Coevolutionary Multiswarm Particle Swarm Optimization 11th

Multi-objective Emergency Resource Dispatch Based on Coevolu...

引用

11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Liu, Si-Chen Chen, Chunhua Zhan, Zhi-Hui Zhang, Jun School of Computer Science and Engineering South China University of Technology Guangzhou51006 China School of Software Engineering South China University of Technology Guangzhou51006 China Victoria University MelbourneVIC8001 Australia

ISBN: (纸本)9783030720612

Emergency resource dispatch plays a significant role in the occurrence of emergency events. An efficient schedule solution not only can deliver the required resources in time, but also can reduce the loss in the disaster area. Recently, many scholars are dedicated to dealing with emergency resource dispatch problem (ERDP) by constructing a model with one objective (e.g., the cost to transport resources or the satisfaction degree of people in the disaster area) and solving the model with single objective optimization algorithms. In this paper, we build a multi-objective model that considers both the cost objective and the satisfaction objective, which takes into account multiple retrieval depots and multiple kinds of resources. We propose to solve this multi-objective ERDP optimization model via the recently famous coevolutionary multiswarm particle swarm optimization (CMPSO) algorithm. Based on multiple populations for multiple objectives (MPMO) framework, the CMPSO algorithm uses two populations to optimize the above two objectives respectively, and leads particles to find Pareto optimal solutions by storing information of different populations in a shared archive. We construct ERDP with various scales to validate the feasibility of the applied CMPSO algorithm. Moreover, by setting the satisfaction objective as the constraint, we also compare the results obtained by CMPSO with those obtained by constrained single objective particle swarm optimization (PSO) algorithm. Experimental results show that: 1) the nondominated solutions obtained by CMPSO perform well in both convergence and diversity on two objectives;2) the results on the cost objective obtained by CMPSO are generally superior to those of PSO under same degree of satisfaction. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

TPE for JPEG images with Dynamic M-ary Decomposition and Adaptive Threshold Constraints

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Ma, Yakun Chai, Xiuli Long, Guoqiang Gan, Zhihua Zhang, Yushu Henan University School of Artificial Intelligence Henan Engineering Research Center for Industrial Internet of Things Zhengzhou450046 China Henan University School of Software Intelligent Data Processing Engineering Research Center of Henan Province Institute of Intelligent Network System Kaifeng475004 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

Traditional JPEG image encryption that prioritizes solely confidentiality fails to account for the pressing usability requirements of cloud-based environments, thus boosting the boom in thumbnail-preserving encryption (TPE) to balance image privacy and usability. However, existing TPE schemes for JPEG images face numerous challenges, such as insufficient security, inability to achieve lossless decryption, and high file extension. To address these challenges, we propose a TPE scheme based on dynamic M-ary decomposition and adaptive threshold constraints (TPE-MDTC). First, the valid ranges of quantized DC coefficients for JPEG images are determined. Then, a sum-preserving encryption method for quantized DC coefficients with compliance threshold constraints is designed using the bit-plane permutation to preserve thumbnails with high accuracy. Next, the introduction of dynamic M-ary decomposition effectively changes bit statistical characteristics preserved by bit-plane permutation, enhancing the ciphertext security. Finally, a quantized AC encryption method with RV (Run/Value) pair global permutation is proposed, effectively modifying the unit block features, thereby significantly improving the security and attack resistance of encrypted images. Experimental results show that the proposed TPE-MDTC scheme can reconstruct the original JPEG images without loss, and the generated ciphertext images exhibit significant advantages over previous schemes regarding file extension and security. © 1991-2012 IEEE.

关键词： Ciphertext

来源：评论

学校读者我要写书评

暂无评论

Mutual Graph Learning Network and Diffusion Probabilistic Model-based Medical Image Segmentation

Mutual Graph Learning Network and Diffusion Probabilistic Mo...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Huaqiang Su Haijun Lei Guoliang Chen Xin Chen Baiying Lei College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Radiology Guangzhou First People’s Hospital Guangzhou China School of Biomedical Engineering & Shenzhen University Medical School Shenzhen University Shenzhen China

Diffusion probabilistic models (DPM) can generate semantically valuable pixel-level representations and are widely used in medical image segmentation tasks. However, DPM faces challenges when dealing with medical image segmentation problems due to the irregular structure of medical images and the similarity between lesions and their surrounding environments. Therefore, this paper proposes a dual-branch Diff-UNet architecture to solve the medical image segmentation problem. Specifically, this architecture introduces the Transformer internal network on top of the standard UNet architecture based on DPM and realizes the interaction of UNet and Transformer branch features through bidirectional connection units to capture local features and remote dependencies better. In addition, through the feature fusion module (FFM), the global context information extracted by DPM is combined with the local detail features captured by the segmentation network. Simultaneously, this paper introduces a mutual graph learning (MGL) network to decompose the image into two task-specific feature maps, which are used to roughly locate the object position and capture the fine details of the object boundary. Finally, the cross attention (CA) module combines the edge information of the diffusion model with the features of the segmentation network to enhance the network’s ability to perceive images. Experiments demonstrate the effectiveness of our Diff-UNet on challenging datasets, including self-collected databases and LUNA16.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Efficient Deep Learning Model based Diagnosis System for Lung Cancer Disease

An Efficient Deep Learning Model based Diagnosis System for ...

引用

International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Gul Zaman Khan Ibrar Ali Shah Farhatullah Muhammad Ikram Ullah Inam Ullah Muhammad Ihtesham Hazrat Junaid Spogmay Yousafzai Fouzia Sardar Department of Computer software Engineering UET Mardan Pakistan School of Automation China University of Geosciences Wuhan China School of software Dalian Jiaotong University Dalian China Department of Computer science Abdul Wali Khan University Maradan Pakistan Department of Computer Science and Information Technology University of Malakand Dir Lower Pakistan Department of zoology University of Malakand Dir. Lower Pakistan

Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable.

关键词： Training Deep learning Computational modeling Lung cancer Lung Feature extraction Mathematical models

来源：评论

学校读者我要写书评

暂无评论

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing

arXiv

引用

arXiv 2024年

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. The code is available at https://***/connorxian/CA-Edit. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Stellar Spectral Classification Based on Capsule Network

引用

Chinese Astronomy and Astrophysics 2021年第3期45卷 352-363页

作者： Li-ting, D.U. Li-hua, H.O.N.G. Jin-tao, Y.A.N.G. Ting-ting, X.U. Jing-min, ZHANG Lin-pin, A.I. Wei-hong, Z.H.O.U. School of Mathematics and Computer Science Yunnan Minzu University Kunming 650500 School of Software Engineering Xiamen Institute of Software Technology Xiamen 361000 Center for Astrophysics Guangzhou University Guangzhou 510006 Key Laboratory of the Structure and Evolution of Celestial Objects Chinese Academy of Sciences Kunming 650011

The rapid development of large-scale sky survey project has produced a large amount of stellar spectral data, which make the automatic classification of stellar spectral data a challenging task. In this paper, we have proposed a stellar spectral classification method based on a capsule network. At first, by using the one-dimensional convolutional network and short-time Fourier transform (STFT), the one-dimensional spectra of the F5, G5, and K5 types selected from the LAMOST Data Release 5 (DR5) are converted into the two-dimensional Fourier spectrum images. Then, the two-dimensional Fourier spectrum images are classified automatically by the capsule network. Because the capsule network can preserve the hierarchical pose relationships among the entities in the image, and it does not need any pooling layers, the experimental results show that the capsule network has a better classification performance, for the classifications of the F5, G5, and K5-type stellar spectra, its classification accuracy is superior to other classification methods. © 2021

关键词： methods: data analysis Stars: fundamental parameters techniques: spectral analysis

来源：评论

学校读者我要写书评

暂无评论

Clustering-Enabled Prioritized Access Control for Massive Machine-Type Communications in Smart Grid

Clustering-Enabled Prioritized Access Control for Massive Ma...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Zhuoyao Shen Zhenyu Liu Qiang Ye Lianming Xu Li Wang School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Department of Computer Science Memorial University of Newfoundland Canada

ISBN: (纸本)9781665454698

In this paper, we propose a massive access control scheme in machine-type communications (MTC) aided smart grid, which prioritizes and clusters the devices based on the latency requirement and the distance between devices. The considered use case features a single cell in the massive connectivity smart grid and a large number of devices with different priority types. For the delay-sensitive devices, a dynamic random access channel (RACH) resource allocation scheme is proposed, where a back-off mechanism is used to defer the access requests of delay-tolerance devices. In addition, we propose a cluster-based congestion control algorithm, which clusters the devices to establish local collaboration. Simulation results show the proposed scheme reduces the average blocking probability, while significantly reducing access delay for delay-sensitive devices compared with state-of-the-art access control methods.

关键词： Access control Vehicular and wireless technologies Simulation Heuristic algorithms Clustering algorithms Dynamic scheduling Smart grids

来源：评论

学校读者我要写书评

暂无评论

Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations

arXiv

引用

arXiv 2024年

作者： Chen, Chuan Liao, Tianchi Deng, Xiaojun Wu, Zihou Huang, Sheng Zheng, Zibin School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing China School of Software Engineering Sun Yat-sen University Zhuhai519000 China Zhuhai Key Laboratory of Trusted Large Language Models China School of Systems Science and Engineering Sun Yat-sen University Guangzhou510006 China

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and collaboratively train models across multiple clients with different data distributions, model structures, task objectives, computational capabilities, and communication resources. This diversity leads to significant heterogeneity, which increases the complexity of model training. In this paper, we first outline the basic concepts of heterogeneous FL and summarize the research challenges in FL in terms of five aspects: data, model, task, device and communication. In addition, we explore how existing state-of-the-art approaches cope with the heterogeneity of FL, and categorize and review these approaches at three different levels: data-level, model-level, and architecture-level. Subsequently, the paper extensively discusses privacy-preserving strategies in heterogeneous FL environments. Finally, the paper discusses current open issues and directions for future research, aiming to promote the further development of heterogeneous FL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

2M3DF: Advancing 3D Industrial Defect Detection with Multi Perspective Multimodal Fusion Network

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Asad, Mujtaba Azeem, Waqar Jiang, He Mustafa, Hafiz Tayyab Yang, Jie Liu, Wei Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Lahore Garrison University Department of Software Engineering Lahore54000 Pakistan China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

In the context of Industrial Anomaly Detection (IAD), ensuring the quality of manufactured products is critical. Traditional 2D based methods often fail to capture anomalies present in complex 3D shapes. For effective anomaly detection in 3D shapes, it is essential to incorporate global semantic context, local geometric structure, and color information of the object. To fully leverage these features, we propose a network named 2M3DF, that leverages knowledge from multi-view RGB images and corresponding point cloud information for enhanced anomaly detection performance. Our model initially employs pre-trained feature extractors that generate local features from multi-view RGB images and corresponding point clouds. The novel inter-modality feature representation and fusion module first adapts these inter-modality features and then effectively aligns and aggregates these multimodality features on a pixel-to-point basis. To learn the normality from point-wise fused multimodal features, we fit a multivariate Gaussian distribution to model the normal feature distribution. Comprehensive experimental evaluations using the MVTec3D-AD and Eyecandies dataset validate the effectiveness of our propose model and demonstrate significant improvements in comparison to existing state-of-the-art methods. Our model achieves a 96.6% mean I-AUROC while delivering real-time results. © 1991-2012 IEEE.

关键词： Normal distribution

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：