检索结果-内蒙古大学图书馆

Subnetwork-based federated few-shot semantic segmentation of organ images

Optoelectronics Letters 2025年

作者： Wu Junpeng Zhao Meng Zhang Huanping Key Laboratory of Computer Vision and System of Ministry of Education， School of Computer Science and Engineering， Tianjin University of Technology

Federated learning(FL)， as a distributed learning paradigm， allows multiple medical institutions to collaborate on learning without the need to centralize all client data. However， existing methods pay little attention to more challenging medical image semantic segmentation tasks， especially in the scenario of the imbalanced dataset in federated few-shot learning. In this paper， we propose a subnetwork-based federated few-shot organ image segmentation method. Firstly， individual clients train using local training samples and then upload local model gradients to the server. The server utilizes their respective local model gradients to update the subnetwork maintained on the server and generate aggregation weights for forming personalized model parameters. Through this method， we can learn the similarities between different clients to address data heterogeneity issues. In addition， to enhance the communication efficiency between clients and the server， we have also designed a personalized layer aggregation strategy， which only transmits partial layer model parameters during the communication process to improve communication efficiency. Finally， we conducted experiments on ABD-MRI and ABD-CT datasets to demonstrate the effectiveness of our method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Review on Binary Code Analysis Datasets 18th

A Review on Binary Code Analysis Datasets

引用

18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

AccelES: Accelerating Top-K SpMV for Embedding Similarity via Low-bit Pruning 31

AccelES: Accelerating Top-K SpMV for Embedding Similarity vi...

引用

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

作者： Zhai, Jiaqi Shi, Xuanhua Huang, Kaiyi Ye, Chencheng Hu, Weifang He, Bingsheng Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore

ISBN: (纸本)9798331506476

In the realm of recommendation systems, achieving real-time performance in embedding similarity tasks is often hindered by the limitations of traditional Top-K sparse matrix-vector multiplication (SpMV) methods, which suffer from high latency due to inefficient memory access patterns. This paper identifies these critical gaps and introduces AccelES, a novel approach that significantly enhances the efficiency of Top-K SpMV. Our method employs a two-stage calculation scheme: the first stage utilizes a compact, low-bit dataset to quickly identify the most relevant entries, while the second stage performs full-precision calculations solely on this pruned subset, thereby minimizing computational overhead. Furthermore, AccelES incorporates innovative matrix representations, Ultra-CSR and Random-CSR, which optimize memory bandwidth utilization. Experimental results demonstrate that AccelES accelerates performance, surpassing state-of-the-art FPGA, GPU, and CPU solutions by factors of 3.4×, 2.5×, and 153.3×, respectively, under controlled conditions. These advancements not only enhance processing speed but also significantly improve real-time performance in recommendation systems, establishing AccelES as a pivotal contribution to the field of Top-K sparse matrix-vector multiplication. © 2025 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

A trajectory-level robot skill learning method based on fuzzy systems

引用

science China(Information sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

来源：评论

学校读者我要写书评

暂无评论

OHMiner: An Overlap-centric system for Efficient Hypergraph Pattern Mining 25

OHMiner: An Overlap-centric System for Efficient Hypergraph ...

引用

20th European Conference on computer systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Qi, Hao Luo, Kang He, Ligang Zhang, Yu Cai, Minzhi Dai, Jingxin He, Bingsheng Jin, Hai Zhang, Zhan Zhao, Jin Yue, Hengshan Yu, Hui Liao, Xiaofei National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom National University of Singapore Singapore Zhejiang Lab China Jilin University China

ISBN: (纸本)9798400711961

Hypergraph Pattern Mining (HPM) aims to identify all the instances of user-interested subhypergraphs (patterns) in hypergraphs, which has been widely used in various applications. However, existing solutions either need significant enumeration overhead because they extend subhypergraphs at the granularity of vertices, or suffer from massive redundant computations because they often need to repeatedly fetch and process the same incident hyperedges for different vertices. This paper presents an overlap-centric system named OHMiner to efficiently support HPM. OHMiner proposes an overlap-centric execution model to determine the subhypergraphs isomorphism through computing and comparing overlaps among hyperedges using set operations. This model aims to efficiently handle the vertices that collectively share the same incident hyperedges. To automatically and precisely retrieve an arbitrary pattern’s overlapping semantics without performing redundant set computations, OHMiner further proposes a redundancy-free compiler, which constructs an Overlap Intersection Graph (OIG) for the pattern, optimizes the OIG, and generates an overlap-centric execution plan to guide the procedure of HPM. Moreover, OHMiner designs an overlap-centric parallel execution engine, which adopts an incremental overlap-pruned approach to fast validate candidates for HPM. Additionally, it proposes a degree-aware data store to support efficient generation of candidates. Through evaluating OHMiner on a broad range of real-world hypergraphs with various patterns, our experimental results show that OHMiner outperforms the state-of-the-art HPM system by 5.4×-22.2×. © 2025 Copyright held by the owner/author(s).

关键词： Compiler

来源：评论

学校读者我要写书评

暂无评论

Towards High-throughput and Low-latency Billion-scale Vector Search via CPU/GPU Collaborative Filtering and Re-ranking 23

Towards High-throughput and Low-latency Billion-scale Vector...

引用

23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present FusionANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of FusionANNS lies in CPU/GPU collaborative filtering and re-ranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. © 2025 FAST. All Rights Reserved.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

引用

1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Positioning Control for Voice Coil Motor Using Unbalanced Multilevel Inverter Topology

Positioning Control for Voice Coil Motor Using Unbalanced Mu...

引用

2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Takei, Gaku Obara, Hidemine Haneda, Ryo Nozaki, Takahiro Graduate School of Science and Technology Keio University Yokohama Japan Yokohama National University Department of Physics Electrical and Computer Engineering Yokohama Japan Keio University Department of System Design Engineering Yokohama Japan

ISBN: (纸本)9798331533892

This article proposes a new multi-level inverter (MLI) based on flying capacitor inverter (FCI), with focused on high-precision positioning control for a voice coil motor (VCM). Multi-level inverters (MLIs) have the characteristic that increasing the number of output voltage levels reduces the output voltage error. However, as the number of levels increases, the number of circuit elements increases, and the circuit becomes more complex. In addition, the output voltage amplitude for each level is constant in conventional inverters. As a result, such as high-precision positioning control of VCM, designing an appropriate inverter for a reference wave that contains a large amount of low-voltage components increases the number of circuit elements. Therefore, this paper proposes a MLI with two unbalanced dc-input voltages, referred to as unbalanced MLIs (UMLIs) hereinafter. This topology uses two different dc-input voltages, and it is possible to reduce the output voltage level in extremely low-voltage range compared to other voltage levels. Simulation results when the high-precision positioning control of the VCM confirm that the proposed UMLIs improves the control performance compared to the conventional FCIs with equal number of output voltage levels and uniform level widths. © 2025 IEEE.

关键词： motion control Multi-level inverter voice coil motor

来源：评论

学校读者我要写书评

暂无评论

Location Privacy Preservation Crowdsensing With Federated Reinforcement Learning

引用

IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 1877-1894页

作者： You, Zhichao Dong, Xuewen Liu, Ximeng Gao, Sheng Wang, Yongzhi Shen, Yulong Xidian University School of Computer Science and Technology The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi'an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Fuzhou University College of Computer and Data Science Fuzhou350025 China Central University of Finance and Economics School of Information Beijing102206 China California State Polytechnic University Pomona Department of Computer Information Systems PomonaCA91768 United States Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xi'an710071 China

Crowdsensing has become a popular method of sensing data collection while facing the problem of protecting participants' location privacy. Existing location-privacy crowdsensing mechanisms focus on static tasks and participants without considering sensing tasks' time requirements and participants' mobility, which cannot achieve satisfactory collected data quality and task completion in crowdsensing with dynamic tasks and participants. Inspired by this, we proposed a location-preservation crowdsensing mechanism, FedSense, considering dynamic tasks and participants based on federated learning (FL) and reinforcement learning (RL). In FedSense, through RL's outstanding decision-making ability, participants select sensing tasks to perform by well-trained RL models without uploading location information to servers for task allocation. We propose an independent tasks selection environment that defines actions, states, and rewards of RL to enable FedSense to achieve satisfactory task completion and data quality while preserving location privacy. Besides, FedSense applies an asynchronous FL aggregation algorithm that reduces participants' network stabilization and device computing ability requirements. Analysis proves that participants' location information does not leave the local device during the model training and task selection process, effectively avoiding privacy leakage. Simulation shows that compared with existing location-preservation crowdsensing mechanisms, FedSense achieves the highest task completion and sensing accuracy for dynamic tasks and participants. © 2024 IEEE.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

A Dynamic Workload Prediction and Distribution in Cloud Computing using Deep Reinforcement Learning and LSTM

引用

Journal of Harbin Institute of Technology(New series) 2025年

作者： Nampally Vijay Kumar Satarupa Mohanty Prasant Kumar Pattnaik School of Computer Engineering KIIT Deemed to be University Department of Computer Science and Business System B V RAJU Institute of Technology

Maintaining high-quality service supply and sustainability in modern cloud computing is essential to ensuring optimal system performance and energy efficiency. A novel approach is introduced in this study to decrease a system.s overall delay and energy consumption by utilising a deep reinforcement learning(DRL)model to predict and allocate incoming workloads flexibly. The proposed methodology integrates workload prediction utilising long short-term memory(LSTM)networks with efficient load-balancing techniques led by deep Q-learning and Actor-Critic algorithms. By continuously analysing current and historical data,the model can efficiently allocate resources,prioritizing speed and energy preservation. The experimental findings demonstrate that our load balancing system,which utilises DRL,significantly reduces average response times and energy usage compared to traditional methods. This approach provides a scalable and adaptable strategy for enhancing cloud infrastructure performance. It consistently provides reliable and durable performance across a range of dynamic workloads.

关键词：

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：