检索结果-内蒙古大学图书馆

APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

Age-Based Scheduling for Mobile Edge computing: A Deep Reinforcement Learning Approach

arXiv

引用

arXiv 2023年

作者： He, Xingqiu You, Chaoqun Quek, Tony Q.S. Intelligent Networking and Computing Research Center School of Computer Science Fudan University Shanghai China Singapore University of Technology and Design Singapore487372 Singapore Yonsei Frontier Lab Yonsei University Korea Republic of

With the rapid development of Mobile Edge computing (MEC), various real-time applications have been deployed to benefit people’s daily lives. The performance of these applications relies heavily on the freshness of collected environmental information, which can be quantified by its Age of Information (AoI). In the traditional definition of AoI, it is assumed that the status information can be actively sampled and directly used. However, for many MEC-enabled applications, the desired status information is updated in an event-driven manner and necessitates data processing. To better serve these applications, we propose a new definition of AoI and, based on the redefined AoI, we formulate an online AoI minimization problem for MEC systems. Notably, the problem can be interpreted as a Markov Decision Process (MDP), thus enabling its solution through Reinforcement Learning (RL) algorithms. Nevertheless, the traditional RL algorithms are designed for MDPs with completely unknown system dynamics and hence usually suffer long convergence times. To accelerate the learning process, we introduce Post-Decision States (PDSs) to exploit the partial knowledge of the system’s dynamics. We also combine PDSs with deep RL to further improve the algorithm’s applicability, scalability, and robustness. Numerical results demonstrate that our algorithm outperforms the benchmarks under various scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking

arXiv

引用

arXiv 2025年

作者： Gao, Kejia Zhou, Liguo Liu, Mingjun Knoll, Alois Technical University of Munich Germany School of Computer Science Nanjing University of Information Science and Technology China Anhui Engineering Research Center for Intelligent Computing and Information Innovation Fuyang Normal University China

End-to-end learning has shown great potential in autonomous parking, yet the lack of publicly available datasets limits reproducibility and benchmarking. While prior work introduced a visual-based parking model and a pipeline for data generation, training and close-loop test, the dataset itself was not released. To bridge this gap, we create and open-source a high-quality dataset for end-to-end autonomous parking. Using the original model, we achieve an overall success rate of 85.16% with lower average position and orientation errors (0.24 meters and 0.34 degrees). The dataset can be found in https://***/s/16LSSfF Qar3e-s dseDt1g?pwd=wybs. © 2025, CC BY-SA.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning

arXiv

引用

arXiv 2023年

作者： Hu, Jingzhi Chen, Zhe Zheng, Tianyue Schober, Robert Luo, Jun School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Intelligent Networking and Computing Research Center School of Computer Science Fudan University Shanghai200438 China The Institute of Digital Communications Friedrich–Alexander University of Erlangen–Nuremberg Erlangen91058 Germany

Positioning is an essential service for various applications and is expected to be integrated with existing communication infrastructures in 5G and 6G. Though current WiFi and cellular base stations (BSs) can be used to support this integration, the resulting precision is unsatisfactory due to the lack of precise control of the wireless signals. Recently, BSs adopting reconfigurable holographic surfaces (RHSs) have been advocated for positioning as RHSs’ large number of antenna elements enable generation of arbitrary and highly-focused signal beam patterns. However, existing designs face two major challenges: i) RHSs only have limited operating bandwidth, and ii) the positioning methods cannot adapt to the diverse environments encountered in practice. To overcome these challenges, we present HoloFed, a system providing high-precision environment-adaptive user positioning services by exploiting multi-band (MB)RHS and federated learning (FL). For improving the positioning performance, a lower bound on the error variance is obtained and utilized for guiding MB-RHS’s digital and analog beamforming design. For better adaptability while preserving privacy, an FL framework is proposed for users to collaboratively train a position estimator, where we exploit the transfer learning technique to handle the lack of position labels of the users. Moreover, a scheduling algorithm for the BS to select which users train the position estimator is designed, jointly considering the convergence and efficiency of FL. Our performance evaluation based on simulations confirms that HoloFed achieves a 57% lower positioning error variance compared to a beam-scanning baseline and can effectively adapt to diverse environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

Adaptive Multi-Scale Language Reinforcement for Multimodal Named Entity Recognition

引用

IEEE Transactions on Multimedia 2025年

作者： Li, Enping Li, Tianrui Luo, Huaishao Chu, Jielei Duan, Lixin Lv, Fengmao Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education School of Computing and Artificial Intelligence Chengdu611756 China JD AI Research Beijing China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

Over the recent years, multimodal named entity recognition has gained increasing attentions due to its wide applications in social media. The key factor of multimodal named entity recognition is to effectively fuse information of different modalities. Existing works mainly focus on reinforcing textual representations by fusing image features via the cross-modal attention mechanism. However, these works are limited in reinforcing the text modality at the token level. As a named entity usually contains several tokens, modeling token-level inter-modal interactions is suboptimal for the multimodal named entity recognition problem. In this work, we propose a multimodal named entity recognition approach dubbed Adaptive Multi-scale Language Reinforcement (AMLR) to implement entity-level language reinforcement. To this end, our model first expands token-level textual representations into multi-scale textual representations which are composed of language units of different lengths. After that, the visual information reinforces the language modality by modeling the cross-modal attention between images and expanded multi-scale textual representations. Unlike existing token-level language reinforcement methods, the word sequences of named entities can be directly interacted with the visual features as a whole, making the modeled cross-modal correlations more reasonable. Although the underlying entity is not given, the training procedure can encourage the relevant image contents to adaptively attend to the appropriate language units, making our approach not rely on the pipeline design. Comprehensive evaluation results on two public Twitter datasets clearly demonstrate the superiority of our proposed model. © 2025 IEEE.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

引用

computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

来源：评论

学校读者我要写书评

暂无评论

Capacity Maximization for Double Passive/Active RIS-Aided MISO Systems in Multi-User Scenarios

引用

IEEE Internet of Things Journal 2025年

作者： Peng, Xin Liu, Chenyu Yu, Chao Xie, Wenwu Yang, Liang Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Hunan Engineering Research Center for Autonomous Data Perception and Processing in Industrial Intelligent Networking Yueyang414006 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This paper addresses the problem of capacity maximization in double reconfigurable intelligent surface (RIS) -aided multiple-input single-output (MISO) systems under multi-user scenarios. A system model is proposed for double RIS-aided MISO configurations, incorporating both passive RIS (PRIS) and active RIS (ARIS). The primary objective is to maximize the minimum channel capacity across all users, and a corresponding optimization problem is formulated to achieve this goal. To overcome the challenges posed by the fractional structure of the non-convex objective function, auxiliary variables are introduced, and the bisection method is utilized. Given the highly coupled and nonconvex nature of the optimization variables, a joint alternating optimization (AO) algorithm is designed based on the bisection method and successive convex approximation (SCA), enabling iterative determination of the beamforming vectors at the base station and the reflection coefficients of RISs. The results indicate that deploying double RISs substantially enhances the system’s channel capacity through reflected links. Notably, the ARIS protocols yield higher performance gains, particularly under scenarios involving high total power and large amplification factors. In multi-user environments, the double RIS configuration significantly outperforms the single RIS setup due to the increased degrees of freedom, especially when supported by ARIS protocols. These findings underscore the substantial potential and advantages of double RIS configurations in complex communication environments. © 2014 IEEE.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

引用

IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A portable,low-cost lactate measurement system

引用

Journal of Measurement science and Instrumentation 2025年第1期16卷 37-46页

作者： WANG Qingqing PAN Yu YU Chuanxin WANG Yanyan ZHANG Kaikai LIU Sheng School of Computer Science and Technology Huaibei Normal UniversityHuaibei 235000China Anhui Provincial Engineering Research Center of Cognitive Behavioral Intelligent Computing and Application Huaibei 235000China

Lactate,as a metabolite,plays a significant role in a number of fields,including medical diagnostics,exercise physiology and food *** methods for lactate measurement often involve expensive and cumbersome *** study developed a portable and low-cost lactate measurement system,including independently detectable hardware circuits and user-friendly embedded software,computer,and smartphone *** experiment verified that the relative error of the detection current in the device circuit was less than 1%.The electrochemical performance was measured by comparing the[Fe(CN)_(6)]^(3−)/[Fe(CN)_(6)]^(4−)solution with the desktop electrochemical workstation CHI660E,and a nearly consistent chronoamperometry(CA)curve was *** modified lactate sensors were used for CA testing of *** the concentration range of 0.1 mmol·L^(−1)to 20 mmol·L^(−1),there was a good linear relationship between lactate concentration and steady-state current,with a correlation coefficient(R2)greater than 0.99 and good repeatability,demonstrating the reliability of the developed *** lactate measurement system developed in this study not only provides excellent detection performance and reliability,but also achieves portability and low cost,providing a new solution for lactate measurement.

关键词： lactate measurement portable device embedded development lactate sensor electrochemical analysis

来源：评论

学校读者我要写书评

暂无评论

PA&DA: Jointly Sampling PAth and DAta for Consistent NAS

PA&DA: Jointly Sampling PAth and DAta for Consistent NAS

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Shun Lu Yu Hu Longxing Yang Zihao Sun Jilin Mei Jianchao Tan Chengru Song Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Kuaishou Technology

Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. However, several works have pointed out that the shared weights suffer from different gradient descent directions during training. And we further find that large gradient variance occurs during supernet training, which degrades the supernet ranking consistency. To mitigate this issue, we propose to explicitly minimize the gradient variance of the supernet training by jointly optimizing the sampling distributions of PAth and DAta (PA&DA). We theoretically derive the relationship between the gradient variance and the sampling distributions, and reveal that the optimal sampling probability is proportional to the normalized gradient norm of path and training data. Hence, we use the normalized gradient norm as the importance indicator for path and training data, and adopt an importance sampling strategy for the supernet training. Our method only requires negligible computation cost for optimizing the sampling distributions of path and data, but achieves lower gradient variance during supernet training and better generalization performance for the supernet, resulting in a more consistent NAS. We conduct comprehensive comparisons with other improved approaches in various search spaces. Results show that our method surpasses others with more reliable ranking performance and higher accuracy of searched architectures, showing the effectiveness of our method. Code is available at https://***/ShunLu91/PA-DA.

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：