检索结果-内蒙古大学图书馆

Preserving Location Privacy of IoT Devices in Heterogeneous Edge computing Architecture Through Deniability-Based Authentication

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ikram Li, Jianqiang Chen, Jie Chen, Yong Ullah, Shamsher Wakeel, Abdul Shenzhen University National Engineering Laboratory of Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National University of Sciences and Technology Military College of Signals Department of Electrical Engineering Islamabad Pakistan

Edge computing moves cloud services closer to consumer Internet of Things (IoT) devices, reducing latency and bandwidth usage. This setup enables faster responses but also introduces new security challenges, particularly concerning location privacy when sender and receiver use different security techniques. The authentication process in such architectures can expose the location of IoT devices. To address this issue, researchers have proposed various privacy-preserving schemes. However, these are computationally heavy for IoT devices. To tackle this issue, we propose two novel heterogeneous deniable authentication schemes: HDA-IoT-I and HDA-IoT-II. These schemes use public-key infrastructure and identity-based cryptography to protect the location privacy of IoT devices in heterogeneous edge computing environments. Our schemes enable the designated edge device to verify the source of a message without being able to prove its origin to a third party, thus preserving privacy. Additionally, they incorporate a batch verification method, which speeds up the verification of multiple deniable authenticators. The proposed schemes are formally proven secure in the random oracle model based on the hardness assumption of elliptic curve inverse computational Diffie-Hellman problem. The performance evaluation demonstrate that our schemes significantly enhance computational and communication efficiency, making them suitable for IoT devices. © 2025 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction 42

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

引用

42nd Chinese Control Conference, CCC 2023

作者： Yan, Weidong Xu, Zhaoliang Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Advanced Institute of Information Technology Peking University Hangzhou310000 China

ISBN: (纸本)9789887581543

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

A Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition 42

A Multi-Head Convolution Network with Attention Consistency ...

引用

42nd Chinese Control Conference, CCC 2023

作者： Liu, Wenkang Sun, Mingyi Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Peking University Advanced Institute of Information Technology Hangzhou310000 China

ISBN: (纸本)9789887581543

In recent years, the demand for facial expression recognition applications has increased rapidly, and its research has received extensive attention from researchers. However, the current recognition methods based on deep learning ignore the idea of multiple head attention and semantic consistency, resulting in the model only paying attention to the local area of the feature map. In addition, the model's inconsistent attention to the images before and after the flip, resulting the poor robustness, poor interpretation, and other shortcomings in the model. To address the above problems, we propose an Affinity Separation Loss (ASLoss), which improves the separability of samples through clustering. Moreover, a Separate Multi-head Attention block (SMA), and a Zonal Loss (ZLoss) are also designed to decentralize the model's attention. Experimental results demonstrate that our proposed MACNet method achieves competitive recognition performance on two public datasets RAF-DB and FERPlus. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Exploring Innovations in Autonomous Robotics and Mechatronics: Application in Manufacturing and Healthcare

Exploring Innovations in Autonomous Robotics and Mechatronic...

引用

Smart Electronics and Communication (ICOSEC), International Conference on

作者： T.Grace Shalini L. Ganeshabu Priyanka Chemudugunta Nagendar Yamsani Ch.Ashok Kumar A. Athiraja Department of Computational Intelligence School of Computing SRM Institute of Science and Technology (SRMIST)Kattankulathur Chengalpattu Tamil Nadu India Department of Robotics and Automation Rajalakshmi Engineering College Rajalakshmi Nagar Thandalam Mevalurkuppam Chennai Tamil Nadu India Department of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of School of Computer Science and Artificial Engineering SR University Warangal India Department of Mechanical Engineering Malla Reddy Engineering College Main Campus Maisammaguda Hyderabad Telangana India Department of CSE (AIML) Malla Reddy College of Engineering Secunderabad Telangana India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics.

关键词： Industries Technological innovation Mechatronics Service robots Scalability Medical services Manufacturing Adaptive control Tuning Testing

来源：评论

学校读者我要写书评

暂无评论

Distributed Load Shedding via Differentially Private Average Consensus Algorithm

Distributed Load Shedding via Differentially Private Average...

引用

第34届中国控制与决策会议

作者： Zixin Qiao Fanghong Guo Xiping Pan Yue Sun Lei Wang Shanghai Institute of Computing Technology Department of Automation Zhejiang University of Technology College of Control Science and Engineering Zhejiang University

This paper presents a distributed load shedding strategy for islanded active distribution power system,where the average consensus algorithm acts as a key tool to discover the required global information for load *** privacy concerns,we propose a new differentially private average consensus algorithm,where a distributed shuffling mechanism based on the Pailler cryptosystem is established to initialize the initial states for the subsequent stage of running standard average *** the proposed algorithm,both rigorous provable differential privacy and accuracy guarantee are established,with an improved privacy/accuracy trade-off than the existing relevant ***,it is shown that given any expected differential privacy levels,the achievable mean-square computation error is inversely proportional to the network size,in contrast with the optimal error achieved in the literature that is independent of the network size.A numerical example and an application to the distributed load shedding for an IEEE 33-bus power system are used to validate the effectiveness of the proposed approach.

关键词： Distributed load shedding average consensus differential privacy power system

来源：评论

学校读者我要写书评

暂无评论

An Iterative Optimizing Framework for Radiology Report Summarization With ChatGPT

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4163-4175页

作者： Ma, Chong Wu, Zihao Wang, Jiaqi Xu, Shaochen Wei, Yaonai Liu, Zhengliang Zeng, Fang Jiang, Xi Guo, Lei Cai, Xiaoyan Zhang, Shu Zhang, Tuo Zhu, Dajiang Shen, Dinggang Liu, Tianming Li, Xiang Northwestern Polytechnical University School of Automation Xi'an710072 China University of Georgia School of Computing AthensGA30602 United States Northwestern Polytechnical University School of Computer Science Xi'an710072 China Massachusetts General Hospital Department of Radiology BostonMA02114 United States University of Electronic Science and Technology of China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu611731 China The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonVA76019 United States ShanghaiTech University School of Biomedical Engineering Shanghai201210 China Shanghai United Imaging Intelligence Company Ltd. Department of Research and Development Shanghai200030 China Shanghai Clinical Research and Trial Center Shanghai201210 China

The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Typically written by radiologists, this part is derived from the 'Findings' section, which can be laborious and error-prone. Although deep-learning-based models, such as bidirectional encoder representation from transformers (BERT), have achieved promising results in automatic impression generation (AIG), such models often require substantial amounts of medical data and have poor generalization performance. Recently, large language models (LLMs) like Chat Generative Pre-trained Transformer (ChatGPT) have shown strong generalization capabilities and performance, but their performance in specific domains, such as radiology, remains under-investigated and potentially limited. To address this limitation, we propose ImpressionGPT, leveraging the contextual learning capabilities of LLMs through our dynamic prompt and iterative optimization algorithm to accomplish the AIG task. ImpressionGPT initially employs a small amount of domain-specific data to create a dynamic prompt, extracting contextual semantic information closely related to the test data. Subsequently, the iterative optimization algorithm automatically evaluates the output of LLMs and provides optimization suggestions, continuously refining the output results. The proposed ImpressionGPT model achieves superior performance of AIG task on both the Medical Information Mart for Intensive Care - Chest X-ray database (MIMIC-CXR) and Open Access Biomedical Image Search Engine (OpenI) datasets without requiring additional training data or fine-tuning the LLMs. This work presents a paradigm for localizing LLMs that can be applied in a wide range of similar application scenarios, bridging the gap between general-purpose LLMs and the specific language processing needs of various domains. © 2020 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge computing, its Applications, and Future Research Trajectories

arXiv

引用

arXiv 2024年

作者： Yang, Ning Chen, Shuo Zhang, Haijun Berry, Randall The Institute of Automation Chinese Academy of Sciences Beijing100190 China The Department of Electrical and Electronic Engineering Imperial College London LondonSW72BX United Kingdom The Department of Computing and Communication Engineering Beijing University of Science and Technology Beijing100083 China The Department of Electrical and Computer Engineering Northwestern University Chicago60208 United States

Mobile Edge computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected things" within edge networks. The advent of applications necessitating real-time, high-quality service presents several challenges, such as low latency, high data rate, reliability, efficiency, and security, all of which demand resolution. The incorporation of reinforcement learning (RL) methodologies within MEC networks promotes a deeper understanding of mobile user behaviors and network dynamics, thereby optimizing resource use in computing and communication processes. This paper offers an exhaustive survey of RL applications in MEC networks, initially presenting an overview of RL from its fundamental principles to the latest advanced frameworks. Furthermore, it outlines various RL strategies employed in offloading, caching, and communication within MEC networks. Finally, it explores open issues linked with software and hardware platforms, representation, RL robustness, safe RL, large-scale scheduling, generalization, security, and privacy. The paper proposes specific RL techniques to mitigate these issues and provides insights into their practical applications. Copyright © 2024, 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

Fair Rank Aggregation

arXiv

引用

arXiv 2023年

作者： Chakraborty, Diptarka Das, Syamantak Khan, Arindam Subramanian, Aditya School of Computing National University of Singapore Singapore Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi India Department of Computer Science and Automation Indian Institute of Science Bengaluru India

Ranking algorithms find extensive usage in diverse areas such as web search, employment, college admission, voting, etc. The related rank aggregation problem deals with combining multiple rankings into a single aggregate ranking. However, algorithms for both these problems might be biased against some individuals or groups due to implicit prejudice or marginalization in the historical data. We study ranking and rank aggregation problems from a fairness or diversity perspective, where the candidates (to be ranked) may belong to different groups and each group should have a fair representation in the final ranking. We allow the designer to set the parameters that define fair representation. These parameters specify the allowed range of the number of candidates from a particular group in the top-k positions of the ranking. Given any ranking, we provide a fast and exact algorithm for finding the closest fair ranking for the Kendall tau metric under block-fairness. We also provide an exact algorithm for finding the closest fair ranking for the Ulam metric under strict-fairness, when there are only O(1) number of groups. Our algorithms are simple, fast, and might be extendable to other relevant metrics. We also give a novel meta-algorithm for the general rank aggregation problem under the fairness framework. Surprisingly, this meta-algorithm works for any generalized mean objective (including center and median problems) and any fairness criteria. As a byproduct, we obtain 3-approximation algorithms for both center and median problems, under both Kendall tau and Ulam metrics. Furthermore, using sophisticated techniques we obtain a (3 − Ε)-approximation algorithm, for a constant Ε > 0, for the Ulam metric under strong fairness. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Exploiting 2.5D/3D Heterogeneous Integration for AI computing

Exploiting 2.5D/3D Heterogeneous Integration for AI Computin...

引用

Asia and South Pacific Design automation Conference

作者： Zhenyu Wang Jingbo Sun Alper Goksoy Sumit K. Mandal Yaotian Liu Jae-Sun Seo Chaitali Chakrabarti Umit Y. Ogras Vidya Chhabria Jeff Zhang Yu Cao School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA Department of Electrical and Computer Engineering University of Wisconsin–Madison Madison WI USA Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Electrical and Computing Engineering Cornell Tech NY USA Department of Electrical and Computer Engineering University of Minnesota Twin Cities MN USA

The evolution of AI algorithms has not only revolutionized many application domains, but also posed tremendous challenges on the hardware platform. Advanced packaging technology today, such as 2.5D and 3D interconnection, provides a promising solution to meet the ever-increasing demands of bandwidth, data movement, and system scale in AI computing. This work presents HISIM, a modeling and benchmarking tool for chiplet-based heterogeneous integration. HISIM emphasizes the hierarchical interconnection that connects various chiplets through network-on-package. It further integrates technology roadmap, power/latency prediction, and thermal analysis together to support electro-thermal co-design. Leveraging HISIM with in-memory computing chiplets, we explore the advantages and limitations of 2.5D and 3D heterogenous integration on representative AI algorithms, such as DNNs, transformers, and graph neural networks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PeanutNeRF: 3D Radiance Field for Peanuts

PeanutNeRF: 3D Radiance Field for Peanuts

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Farah Saeed Jin Sun Peggy Ozias-Akins Ye Juliet Chu Changying Charlie Li School of Computing University of Georgia Department of Horticulture Institute of Plant Breeding Genetics and Genomics University of Georgia Department of Agricultural and Biological Engineering University of Florida Biosensing Automation and Intelligence Laboratory University of Florida

Accurate phenotypic analysis can help plant breeders efficiently identify and analyze suitable plant traits to enhance crop yield. While 2D images from RGB cameras are easily accessible, their trait estimation performance is limited due to occlusion and the absence of depth information. On the other hand, 3D data from LiDAR sensors are noisy and limited in their ability to capture very thin plant parts such as peanut plant pegs. To combine the merits of both the 2D and 3D data analysis, the 2D images were used to capture thin parts in peanut plants, and deep learning-based 3D reconstruction using captured 2D images was performed to obtain 3D point clouds with information about the scene from different angles. The neural radiance fields were optimized for implicit 3D representation of the plants. The trained radiance fields were queried for 3D reconstruction to achieve point clouds for a 360-degree view and frontal view of the plant. With frontal-view reconstruction and the corresponding 2D images, we used Frustum PVCNN to perform 3D detection of peanut pods. We showed the effectiveness of PeanutNeRF on peanut plants with and without foliage: it showed negligible noise and a chamfer distance of less than 4 × 10 −4 from a manually cleaned version. The pod detection showed a precision of around 0.7 at the IoU threshold of 0.5 on the validation set. This method can assist in accurate plant phenotypic studies of peanuts and other important crops.

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：