检索结果-内蒙古大学图书馆

14th International Conference on Frontier Computing, FC 2024

作者： Li, Hengdong Ye, Jun Zhang, Dian Trusted and Intelligent Data Analytic Research Group School of Cyberspace Security Hainan University Haikou China Key Laboratory of Internet Information Retrieval of Hainan Province Hainan University Haikou China

ISBN: (纸本)9789819627974

Due to the special and dangerous nature of flammable and explosive petrochemical substances, petrochemical storage and transportation involves great risks and is highly prone to fire and explosion and other accidents, which poses a great challenge to firefighting and rescue. Due to the special characteristics of petrochemical fire accidents, traditional fire training and experience often cannot cope with them, resulting in low firefighting efficiency and heavy economic losses. With the wide application of virtual reality technology in the training field, this study summarises the current research status in the field of virtual reality firefighting, optimises the LOD technology, and develops a petrochemical firefighting training system based on virtual reality, and the research results effectively address the current shortcomings in petrochemical firefighting training. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fire extinguishers

来源：评论

学校读者我要写书评

暂无评论

Federated Hybrid-Supervised Learning for Universal Medical Image Segmentation

Federated Hybrid-Supervised Learning for Universal Medical I...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Shenhai Wen, Sian Li, Congyu Chen, Qing Li, Laquan College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China School of Science Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350368741

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://***/TrivenDev/FedSLAG. © 2025 IEEE.

关键词： federated learning hybrid-supervised learning medical image segmentation

来源：评论

学校读者我要写书评

暂无评论

Personalized Review Recommendation based on Implicit dimension mining

Personalized Review Recommendation based on Implicit dimensi...

引用

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Xu, Bei Xu, Yifan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

Users usually browse product reviews before buying products from e-commerce websites. Lots of e-commerce websites can recommend reviews. However, existing research on review recommendation mainly focuses on the general usefulness of reviews and ignores personalized and implicit requirements. To address the issue, we propose a Large language model driven Personalized Review Recommendation model based on Implicit dimension mining (PRR-LI). The model mines implicit dimensions from reviews and requirements, and encodes them in the form of "text + dimension". The experiments show that our model significantly outperforms other state-of-the-art textual models on the Amazon-MRHP dataset, with some of the metrics outperforming the state-of-the-art multimodal models. And we prove that encoding "text + dimension" is better than encoding "text" and "dimension" separately in review recommendation. © 2024 Association for Computational Linguistics.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

Conditional selection with CNN augmented transformer for multimodal affective analysis

引用

CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

来源：评论

学校读者我要写书评

暂无评论

Multi-Granular Genetic Algorithm Based Task Allocation for Heterogeneous UAVs

Multi-Granular Genetic Algorithm Based Task Allocation for H...

引用

International Conference on big data and Information Analytics (bigDIA)

作者： Jiajia Cao Guoyin Wang Qun Liu Haihuan Jiang Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

The unmanned aerial vehicles (UAVs) cooperative multi-task allocation systems have been widely used in recent years in areas such as land aerial photography, transportation of materials in disaster areas and so on. With the increase of the complex relationship between the mission and the UAV, it is difficult for genetic algorithm to find the global optimal solution in the search process. This paper proposes a new Multi-Granular Genetic Algorithm (MGGA) to solve the UAV cooperative multi-task allocation problem (CMTAP), accounting for multiple constraints in real scenarios. To enhance the quality of the initial population of genetic algorithm, a data preprocessing method via a typical clustering algorithm is proposed at first. Aiming to improve the solution speed of the standard genetic algorithm, MGGA incorporates the concept of Multi-Granular Cognition and combines granular-ball computing with the genetic algorithm to solve the CMTAP. Finally, through simulation experiments in different scenarios, the algorithm is proved to have better optimization ability and convergence effect.

关键词： Computational modeling data preprocessing Clustering algorithms Transportation System recovery Autonomous aerial vehicles Multitasking Resource management Standards Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Collaborative Optimization of Truck-Drone Based on Multi-agent Reinforcement Learning 11th

Collaborative Optimization of Truck-Drone Based on Multi-a...

引用

11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Zhang, Wenhao Wang, Guoyin Liu, Qun Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing 400065 China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing 401331 China

ISBN: (纸本)9783031927409

This study proposes a novel Multi-Head Attention-enhanced Proximal Policy Optimization (MHA-PPO) algorithm for collaborative truck-drone delivery systems in dynamic logistics scenarios. To address the limitations of traditional multi-agent reinforcement learning in handling complex spatiotemporal dependencies and real-time decision-making, we integrate a multi-head attention mechanism into the PPO framework, enabling adaptive feature fusion of heterogeneous agent observations (e.g., drone positions, truck routes, and package urgency levels). A key innovation lies in the design of a variable emergency mechanism that models a random urgency generation module and dynamically adjusts task priorities based on the urgency of customer demand for packages. The system is implemented in a high-fidelity Unity simulation environment incorporating 3D urban terrain, realistic drone aerodynamics, and traffic flow patterns. Comparative experiments against baseline algorithms (PPO, Multi-Agent Proximal Policy Optimization (MAPPO), Multi-Head Attention Deep Deterministic Policy Gradient (MHA-DDPG)) demonstrate that our MHA-PPO achieves superior performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi-agent problem Reinforcement learning Task allocation Truck-drone cooperative delivery

来源：评论

学校读者我要写书评

暂无评论

CoGAP: A Personalized Federated Learning Method Using Collaborative Optimization for Medical Image Classification

CoGAP: A Personalized Federated Learning Method Using Collab...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Shenhai Li, Congyu Wen, Sian Gao, Xi Yu, Lei College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (纸本)9798350368741

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L2 regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://***/lcyCQUPT/CoGAP. © 2025 IEEE.

关键词： collaborative optimization federated learning medical image classification

来源：评论

学校读者我要写书评

暂无评论

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

引用

Computers, Materials & Continua 2024年第4期79卷 1105-1135页

作者： Lin Wang Juan Zhao Hu Song Xiaolong Xu Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing210042China School of Network Security Jinling Institute of TechnologyNanjing211169China State Grid Jiangsu Electric Power Company Limited Nanjing210000China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210042China

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching ***,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design refe

关键词： Multi-face expression recognition smart classroom end-to-end detection group emotion assessment

来源：评论

学校读者我要写书评

暂无评论

Simulating Bluetooth virus propagation on the real map via infectious attenuation algorithm and discrete dynamical system

引用

Iran Journal of Computer Science 2024年第4期7卷 755-776页

作者： Zhu, Qingyi Wan, Wentao Gan, Chengquan Fan, Ziqi Yang, Lu-Xing School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing 400065 China School of Information Technology Deakin University Melbourne 3125 VIC Australia

In recent years, mobile smart devices have gained popularity globally due to their convenience and efficiency. However, this popularity has also attracted the attention of virus creators, resulting in a significant cost to users. Current research on virus propagation primarily focuses on developing anti-virus software and macroscopic modeling. Software development often lags behind and only begins when viruses become a significant threat. In contrast, macroscopic modeling frequently deviates from reality as real networks do not completely conform to the characteristics of scale-free or small-world networks. Furthermore, special factors such as device mobility and signal attenuation must be considered when studying Bluetooth-transmitted viruses. Traditional research methods have shown limited performance in this field. To address these issues, this paper proposes a method that describes the propagation behavior of Bluetooth viruses in smart devices by utilizing an Infectious Attenuation Algorithm (IAA) and real maps. This method incorporates population density, map contours, and user security awareness to construct a unique discrete system. More importantly, we integrate this system into the map of Deyuan Street in Chengdu City and create a visualization system using the Simulation of Urban Mobility (SUMO) simulation software. In this system, the proposed IAA simulates the decay of virus infectivity based on the distance between devices and the attenuation of signal power between them. Our research approach considers the two core elements of infectivity attenuation and maps, making it more realistic and overcoming the lag in software development. Additionally, the introduction of SUMO for visualization simulation enhances the practical significance of our experiments. Finally, we propose feasible prevention strategies based on comparative experiments, data analysis, and theoretical analysis. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2

关键词： Bluetooth virus Discrete dynamical system Infectious attenuation algorithm Population density Real map security awareness SUMO simulation

来源：评论

学校读者我要写书评

暂无评论

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators 31

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

引用

31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Yang, Siling He, Shuibing Wang, Wenjiong Yin, Yanlong Wu, Tong Chen, Weijian Zhang, Xuechen Sun, Xian-He Feng, Dan The State Key Laboratory of Blockchain and Data Security Zhejiang University China Zhejiang Lab China Institute of Blockchain and Data Security China Zhejiang Key Laboratory of Big Data Intelligent Computing China Washington State University Vancouver United States Illinois Institute of Technology United States Huazhong University of Science and Technology China Wuhan National Laboratory for Optoelectronics China

ISBN: (纸本)9798331506476

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work. © 2025 IEEE.

关键词： Graph neural networks

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：