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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68658 条记录，以下是311-320 订阅

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MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network for Mpox Detection

MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yue, Yubiao Xue, Jun Liang, Haihuang Li, Zhenzhang Wang, Yufeng School of Biomedical Engineering Guangzhou Medical University Guangzhou China School of Computer Science and Technology Anhui University Hefei China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou China Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350368741

Due to the lack of effective mpox detection tools, the mpox virus continues to spread worldwide and has been once again declared a public health emergency of international concern by the World Health Organization. Lightweight deep learning model-based detection systems are crucial to alleviate mpox outbreaks since they are suitable for widespread deployment, especially in resource-limited scenarios. However, the key to its successful application depends on ensuring that the model can effectively model local features and long-range dependencies in mpox lesions while maintaining lightweight. Inspired by the success of Mamba in modeling long-range dependencies and its linear complexity, we proposed a lightweight hybrid architecture called MpoxMamba for efficient mpox detection. MpoxMamba utilizes depth-wise separable convolutions to extract local feature representations in mpox skin lesions and greatly enhances the model's ability to model the global contextual information by grouped Mamba modules. Notably, MpoxMamba's parameter size and FLOPs are 0.77M and 0.53G, respectively. Experimental results on two widely recognized benchmark datasets demonstrate that MpoxMamba outperforms state-of-the-art lightweight models and existing mpox detection methods. Importantly, we developed a web-based online application to provide free mpox detection. The source code of MpoxMamba and the developed online application are available at https://***/YubiaoYue/MpoxMamba. © 2025 IEEE.

关键词： Deep Leraning Lightweight Model Mpox Detection Vision Mamba

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A Reversible Data Hiding in Encryption Domain for JPEG Image Based on Controllable Ciphertext Range of Paillier Homomorphic Encryption Algorithm 21st

A Reversible Data Hiding in Encryption Domain for JPEG Ima...

引用

21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Ma, Bin Zhao, Chunxin Ma, Ruihe Xian, Yongjin Wang, Chunpeng Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Economics Jilin University Changchun130015 China

ISBN: (纸本)9789819601219

With the rapid advancement of cloud computing, significant security concerns regarding data storage have been continuously raised. When the encrypted images are stored in the cloud, reversible data hiding in encrypted domain is used to embed additional data to manage the storage resources in the cloud. Most of the current works are applied to uncompressed images rather than JPEG images. Therefore, this paper proposes a reversible data hiding algorithm for JPEG images based on an enhanced Paillier homomorphic encryption algorithm. In the proposed scheme, the AC coefficients are altered by using a ciphertext distribution constrained paillier homomorphic encryption algorithm, so as to reduce the size of the data-embedded encrypted image. Whereas, the DC coefficients are encrypted by using the one-dimensional tangent chaos encryption algorithm, thereby ensuring high security of the encrypted image. Moreover, according to the characteristics of the homomorphic encryption algorithm, the confidential data can be recovered from both the encryption domain and from the plain domain. Experimental results demonstrate that the ciphertext distribution-constrained Paillier homomorphic encryption algorithm obtains higher data embedding capacity and superior security in comparison with other advanced algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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The Application of Innovative Design Concept of Color and Light and Shadow in Underground Space Lighting 23rd

The Application of Innovative Design Concept of Color and...

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23rd IFIP TC 14 international conference on Entertainment Computing, ICEC 2024

作者： Huang, Langqing Zhang, Lingyan Yao, Cheng Ying, Fangtian Zhejiang University 866 Yuhangtang Hangzhou China Macau University of Science and Technology 999078 China

ISBN: (纸本)9783031743528

This study presents a user-centered approach to enhancing indoor lighting in underground spaces, focusing on the well-being and productivity of occupants while emphasizing energy efficiency and compliance with lighting standards. By integrating design thinking and color theory, the research formulates hypotheses concerning the psychological impacts of lighting, the utility of smart devices for light measurement, and the role of technology in achieving regulatory compliance. Through semi-structured interviews, design strategy proposals, and lighting tests, the study gathers qualitative and quantitative data. The analysis reveals the importance of light distribution, color temperature, and shadow quality in creating functional and engaging environments. The study concludes that an optimized lighting design can significantly improve spatial perception and emotional responses, offering valuable insights for future lighting design in residential and educational spaces. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Regulatory compliance

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DEBT: Enhancing Entity Alignment in Knowledge Graphs through Description Enrichment and Bootstrap Training

DEBT: Enhancing Entity Alignment in Knowledge Graphs through...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xiang, Ting Zhang, Jiapeng Chen, Changjian Tang, Zhuo College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9798350368741

Entity alignment has emerged as a powerful technique for integrating knowledge graphs, facilitating the fusion of heterogeneous knowledge into a unified graph. The state-of-the-art methods combine both graph structures and side information for effective entity alignment. However, they neglect low-quality issues in data. Specifically, the emerging knowledge graphs in diverse fields amass a wealth of entities that lack not only adequate descriptions but also annotated alignments. These two limitations lead to the overfitting problem and degrade the alignment performance. To tackle these challenges, we propose DEBT, an innovative approach that systematically enhances entity alignment. It first enriches the descriptions of entities by aggregating their neighbors and attributes. Then, a bootstrap strategy is utilized to expand the training set by incorporating entity pairs with similarity scores exceeding a dynamically decreasing threshold. Experimental results demonstrate that our method achieves the state-of-the-art accuracy while reducing the number of annotated entity alignment pairs. © 2025 IEEE.

关键词： bootstrap strategy description enrichment entity alignment knowledge graph

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Multi-Grained Feature Pruning for Video-Based Human Pose Estimation

Multi-Grained Feature Pruning for Video-Based Human Pose Est...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Zhigang Fan, Shaojing Liu, Zhenguang Wu, Zheqi Wu, Sifan Jiao, Yingying College of Computer Science and Technology Zhejiang Gongshang University Hangzhou China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Human pose estimation, with its broad applications in action recognition and motion capture, has experienced significant advancements. However, current Transformer-based methods for video pose estimation often face challenges in managing redundant temporal information and achieving fine-grained perception because they only focus on processing low-resolution features. To address these challenges, we propose a novel multi-scale resolution framework that encodes spatio-temporal representations at varying granularities and executes fine-grained perception compensation. Furthermore, we employ a density peaks clustering method to dynamically identify and prioritize tokens that offer important semantic information. This strategy effectively prunes redundant feature tokens, especially those arising from multi-frame features, thereby optimizing computational efficiency without sacrificing semantic richness. Empirically, it sets new benchmarks for both performance and efficiency on three large-scale datasets. Our method achieves a 93.8% improvement in inference speed compared to the baseline, while also enhancing pose estimation accuracy, reaching 87.4 mAP on the PoseTrack2017 dataset. © 2025 IEEE.

关键词： Large datasets

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Anti-concept Drift-enabled Recognition Method of Encrypted Traffic Behavior 24

Anti-concept Drift-enabled Recognition Method of Encrypted T...

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2nd international conference on Communication Networks and Machine Learning, CNML 2024

作者： Zheng, Jicheng Huang, Shuangshuang Ji, Qingbing School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Key Laboratory of Security Communication Sichuan Chengdu China

ISBN: (纸本)9798400711688

Although encrypted traffic ensures information security, it can also be exploited by malicious actors, leading to security risks. This paper addresses the limitations of existing behavior recognition technologies, particularly the concept drift issue in the"training-testing" phase. We propose an Anti-Concept Drift Framework (ACD-WFP), which includes a Confusion-Aware Adversarial Concept Drift Detection Algorithm (CACD) and an Incremental Learning-Based Multi-Modal Behavior Recognition Algorithm (IM-WFP). In experiments involving 75GB of encrypted traffic across 12 concept drift scenarios, CACD efficiently detects and tracks drift with low storage requirements, while IM-WFP improves recognition accuracy by 2% to 22% through continuous learning of new concepts. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Distributed Adaptive Coordination Control for Autonomous Road Traffic at Multiple Intersections

Distributed Adaptive Coordination Control for Autonomous Roa...

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international conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Ji, Honghai Shi, Qirui Liu, Shida Fan, Lingling wang, Li School of Electrical and Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9789819639724

As societal demands on traffic control increase, traditional model-based traffic control methods are showing their limitations. This paper introduces a Distributed Adaptive Coordination Control (DACC) algorithm, which integrates data-driven control and multi-agent systems, aimed at optimizing traffic signal control across multiple intersections to effectively balance queuing times. Through joint simulation experiments with SUMO and Python, and comparison with the Proportional-Integral-Derivative Coordination Control (PIDCC) algorithm, the DACC algorithm has demonstrated advantages in reducing average queuing times and improving traffic flow efficiency. The results indicate that the DACC algorithm excels in managing traffic flow variations. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Traffic signals

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Dynamic Energy Management for Integrated Energy System in Industrial Parks Based on Robust Model Predictive Control

Dynamic Energy Management for Integrated Energy System in In...

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international conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Xu, Jiawen Zhao, Zhuoli Liang, Yu Liu, Chang Yu, Lei Shi, Xuntao Lai, Loi Lei Department of Electrical Engineering School of Automation Guangdong University of Technology Guangzhou510006 China Electric Power Research Institute China Southern Power Grid Guangzhou510663 China

ISBN: (纸本)9789819620456

In this paper, a novel efficient robust model predictive control (RMPC) strategy is proposed for the intraday energy management of IES, which has less conservativeness and more economical performance. Two dimensions of uncertainty factors are introduced to flexibly model the renewable energy and loads and the robust degree can be adjusted according to the operation requirement. A real-time feedback module is introduced to update the allocation plan of the dispatchable units online, which significantly improves the feasibility of the scheduling plan. Comprehensive simulation studies with the comparison of the state-of-the-art methods demonstrate the effectiveness of the proposed RMPC strategy. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Clean energy

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Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning 31

Unifying Dual-Space Embedding for Entity Alignment via Contr...

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31st international conference on Computational Linguistics, COLING 2025

作者： Wang, Cunda Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Entity alignment (EA) aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contain complex local and hierarchical structures, which are hard to represent in a single space. In this paper, we propose a novel method named as UniEA, which unifies dual-space embedding to preserve the intrinsic structure of KGs. Specifically, we simultaneously learn graph structure embeddings in both Euclidean and hyperbolic spaces to maximize the consistency between embeddings in the two spaces. Moreover, we employ contrastive learning to mitigate the misalignment issues caused by similar entities, where embeddings of similar neighboring entities become too close. Extensive experiments on benchmark datasets demonstrate that our method achieves state-of-the-art performance in structure-based EA methods. Our code is available at https://***/wonderCS1213/UniEA. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Design and implementation of Marine biometrics identification and recommendation mini program based on YOLO 24

Design and implementation of Marine biometrics identificatio...

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8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Lin, Meirong Zheng, Zhiguo Wang, Hejia Zhang, Sijin Information Engineering College Hainan Vocational University of Science and Technology Hainan Haikou China School of Information and Communication Engineering Hainan University Haikou Hainan China

ISBN: (纸本)9798400710094

This paper presents a design and implementation scheme of Marine biometrics identification and recommendation mini program based on YOLO object detection algorithm. With the development and utilization of Marine resources, the demand for Marine biological identification and protection applications is increasing, so it is of great significance to design and implement an application that can intelligently identify Marine organisms and recommend high-quality seafood products. There are many limitations in the traditional Marine biometrics, such as fuzzy image and insufficient key technology of target detection. To solve these problems, the project team adopted the improved YOLO algorithm, combining a variety of techniques to improve the accuracy and real-time identification of Marine organisms. The Marine biological recognition and recommendation mini program based on YOLO algorithm can automatically identify the types of Marine organisms in the uploaded pictures, and provide relevant information and suggestions according to the identification results, helping users to understand the overview of Marine organisms more intuitively and clearly, and providing the recommendation function of nearby Marine product merchants. This project verifies the effectiveness and practicability of the mini program through practical application tests in different scenarios, providing the public with a convenient tool to enhance the understanding of Marine biodiversity and support the sustainable development of Marine ecosystems. © 2024 Copyright held by the owner/author(s).

关键词： Biometrics

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