检索结果-内蒙古大学图书馆

A Secure and Efficient Framework for Multi-Round Data Trading Over the Internet of Artificially Intelligent Things

IEEE Internet of Things Magazine 2022年第1期5卷 119-124页

作者： Zheng, Xu Zhang, Lizong Hui, Bei Tian, Ling Cai, Zhipeng School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Information and Software Engineering University of Electronic Science and Technology of China China Georgia State University Department of Computer Science United States

IoT data trading has greatly benefited the popularization of both the Internet of Things (IoT) and Artificial Intelligence of Things (AIoT). Current solutions mainly treat the dataset owned by each device as a commodity and focus on maintaining a data market. Meanwhile, inspired by recent achievements in distributed machine learning and federated learning, AIoT can actually realize its services by retaining the data locally on devices and only exchanging model parameters. However, existing IoT data trading methods fail to meet this secure and efficient process. Therefore, this article proposes a novel framework for data trading over AIoT. It allows the service provider to trade on deep learning model training instead of purchasing the full datasets. Key factors for the security issues and data pricing are addressed for the framework, including the design of the learning parameters and the comprehensive privacy concerns. Then a novel data trading strategy is proposed for the service provider to jointly maintain the model performance and the budget consumption. Evaluation results reveal the improvements on the model performance. Finally, some open problems in the design of data trading in AIoTs are discussed. © 2018 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Reward Maximization for Disaster Zone Monitoring with Heterogeneous UAVs

引用

IEEE/ACM Transactions on Networking 2024年第1期32卷 890-903页

作者： Xu, Wenzheng Wang, Chengxi Xie, Hongbin Liang, Weifa Dai, Haipeng Xu, Zichuan Wang, Ziming Guo, Bing Das, Sajal K. Sichuan University College of Computer Science Chengdu610065 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Nanjing University Department of Computer Science and Technology Nanjing210023 China Dalian University of Technology School of Software Dalian116024 China Sichuan University Key Laboratory of Birth Defects and Related Maternal and Child Diseases West China Second Hospital College of Computer Science Chengdu610066 China Missouri University of Science and Technology Department of Computer Science RollaMO65409 United States

In this paper, we study the deployment of K heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of K heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one type of UAVs can take only visual images while the other type of UAVs can take both visual and thermal infrared images. In this paper, we investigate a problem of scheduling K heterogeneous UAVs to monitor PoIs so that the sum of monitoring rewards received by all UAVs is maximized, subject to energy capacity on each UAV. We propose the very first 13 -approximation algorithm for this scheduling problem. We also evaluate the performance of the proposed algorithm, using real parameters of commercial UAVs. Experimental results show that the performance of the proposed algorithm is promising, which is improved by 25%, compared with existing algorithms. © 1993-2012 IEEE.

关键词： Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Deep learning based water leakage detection for shield tunnel lining

引用

Frontiers of Structural and Civil Engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

来源：评论

学校读者我要写书评

暂无评论

Cooperative Traffic Signal Control with Bus Priority: A Multi-Agent Reinforcement Learning Approach 24

Cooperative Traffic Signal Control with Bus Priority: A Mult...

引用

24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Congyu Wang, Chong Li, Yueqi Ye, Guoqiang Bao, Licheng Wen, Ming Zhang, Jian School of Software Nanjing Univ. of Information Science and Technology Nanjing China School of Computer Science Nanjing Univ. of Information Science and Technology Nanjing China School of Transportation Southeast Univ. Nanjing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Intelligent traffic signal control plays a crucial role in reducing the escalating problem of traffic congestion. However, traditional methods of traffic signal control struggle to effectively adapt to the ever-changing road conditions and demands in complex urban traffic environments. This becomes particularly problematic when considering the punctuality of buses, as it can lead to traffic congestion for other vehicles. To address this issue, this paper introduces a multi-agent reinforcement learning method that prioritizes bus punctuality while minimizing travel time for regular vehicles. We established the SUMO simulation environment with dedicated bus lanes and designed a reward function based on bus arrival and vehicle waiting times. By employing a deep reinforcement learning approach, we aimed to reduce average travel time. The simulation results demonstrate significant improvements, including a 74.44% reduction in vehicle waiting time at intersections and a 78.06% decrease in bus delay time. Notably, even when focusing solely on optimizing vehicle travel time, our approach enhances bus punctuality, resulting in a 57.03% reduction in bus delay time. Thus, we believe that our approach offers a viable solution for addressing bus priority policy challenges. © ASCE.

关键词： Travel time

来源：评论

学校读者我要写书评

暂无评论

M-GhostNet: A Lightweight CNN Model Combined with Coordinate Attention Mechanism for Identifying Pests and Diseases 12

M-GhostNet: A Lightweight CNN Model Combined with Coordinate...

引用

12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Ma, Chi Xiao, Yu Hu, Hui Li, Jingyan Wu, Jie The School of Computer Science and Engineering Huizhou University Huizhou516007 China The School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9798400707988

Plant pests and diseases have always been an important factor affecting agricultural production, and how to automatically identify them is one of the current research hotspots. Although traditional deep learning models can achieve good results, they are difficult to deploy into small devices due to their large number of parameters. In this paper, we design a lightweight convolutional neural network M-GhostNet based on the coordinate attention mechanism, which improves the bottleneck structure in the original GhostNet and and effectively extracts the spatial information of the image. We compare and ablate the M-GhostNet model on the IP102 dataset and the Embrapa dataset. Compared with the existing lightweight neural networks, M-GhostNet achieves the best results of 67.15%, 65.04%, and 71.41% on the IP102 dataset, respectively, and 92.67%, 95.65%, 93.74%, and 96.52% on the Embrapa dataset in terms of Mrec, Mpre, MF1, and Acc. The final results show that M-GhostNet can achieve good performance in both pest and disease identification. © 2023 ACM.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

引用

computers, Materials & Continua 2024年第6期79卷 5201-5223页

作者： Chen Zhang Liming Liu Yufei Yang Yu Sun Jiaxu Ning Yu Zhang Changsheng Zhang Ying Guo Software College Northeastern UniversityShenyang110169China School of Information Science and Engineering Shenyang Ligong UniversityShenyang110159China China Telecom Digital Intelligence Technology Co. Ltd.Beijing100035China College of Computer Science and Engineering Ningxia Institute of Science and TechnologyShizuishan753000China

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave *** preferentially selects the best-performing *** tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search *** address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search ***,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its ***,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test *** results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

关键词： Flying foxes optimization(FFO)algorithm opposition-based learning niching techniques swarm intelligence metaheuristics evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Accelerating Hierarchical Federated Learning with Model Splitting in Edge Computing 30

Accelerating Hierarchical Federated Learning with Model Spli...

引用

30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Wang, Xiangnan Xu, Yang Xu, Hongli Sun, Zhipeng Liao, Yunming Qi, Ji University of Science and Technology of China School of Computer Science and Technology Hefei China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China Software Technology Co. Ltd China

ISBN: (纸本)9798331515966

Recently, Hierarchical Federated Learning (HFL) stands out as a cutting-edge approach to efficiently learn knowledge from massive data on edge devices or clients. To alleviate the computation/communication burden of training large-scale models on resource-constrained clients, Hierarchical Split Federated Learning (HSFL), which splits an entire model into a top and bottom (sub-)model and offloads the training of the top model to the edge server, has been proposed. Nonetheless, there are two key issues, i.e., system heterogeneity and dynamic contexts, hindering the application of effective HSFL. In response, we present an efficient HSFL method, termed AdaHSFL, which introduces intra-cluster gradient feedback regulation and inter-cluster updating frequencies optimization to enhance training efficiency. Concretely, intra-cluster gradient feedback regulation enables edge servers to instantly process incoming smashed data and calculate gradients using top model copies on background threads, while inter-cluster updating frequencies optimization adjusts edge cluster updating frequencies to align the training duration across all clusters with that of the fastest cluster. Furthermore, AdaHSFL explores to jointly implement these two strategies to eliminate the idle waiting time both intra- and intercluster incurred by synchronization barriers. Rigorous performance evaluation demonstrates that AdaHSFL can improve the accuracy by 3.9%-25.5% within given time budgets, compared with the baselines. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Vulnerability Detection of Ethereum Smart Contract Based on SolBERT-BiGRU-Attention Hybrid Neural Model

引用

computer Modeling in Engineering & sciences 2023年第10期137卷 903-922页

作者： Guangxia Xu Lei Liu Jingnan Dong Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China

In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code ***,the time cost of training a language model from zero is very high,and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at *** this paper,we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained languagemodel BERT and connected to a bidirectional gate recurrent *** downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their *** experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities,and it is found that compared with the existing methods,the accuracy of our model can reach 93.85%,and the Micro-F1 Score is 94.02%.

关键词： Smart contract pre-trained language model deep learning recurrent neural network blockchain security

来源：评论

学校读者我要写书评

暂无评论

Crop Pest Image Classification Based on Multi-Scale Convolutional Neural Network 9

Crop Pest Image Classification Based on Multi-Scale Convolut...

引用

9th International Conference on Fuzzy Systems and Data Mining, FSDM 2023

作者： Ma, Chi Li, Huikai Hu, Hui Li, Jingyan Wu, Jie School of Computer Science and Engineering Huizhou University Huizhou516007 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781643684703

Crop diseases and pests are major threats to crop yield and quality, as well as global food security and agricultural livelihoods. Therefore, it is essential to identify crop pests and diseases in a timely, efficient and accurate manner. In this paper, we propose MConvNeXt, a crop pest classification model based on multi-scale feature fusion, which adds a multi-scale module to the original ConvNeXt network model to better capture feature information at different scales. We experimentally analyzed the MConvNeXt model on the IP102 dataset, and the results show that it outperforms the baseline network and other classical image classification models in pest classification. © 2023 The authors and IOS Press.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Voice by the Non-sighted: Practices and Challenges of Audiobook Voice Actors with Blind and Low Vision in China 25

Voice by the Non-sighted: Practices and Challenges of Audiob...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Chen, Shi Zhang, Jingao Lou, Suqi Wang, Xiaodong Xiang, Wei Sun, Lingyun School of Computer Science and Technology Zhejiang University Hangzhou China School of Software Technology ZheJiang University Ningbo China

ISBN: (纸本)9798400713941

Auditory sense is the primary channel for people with blind and low vision (BLV) to access information. This paper aims to understand the productization of individual voices of BLV voice actors in the audiobook industry. We conducted online semi-interviews with the BLV voice actors in China (N = 13) and gained insights into the workflow through offline observations. Interviews indicate that the ability to match job requirements, social benefits, and accessible support are key factors that draw BLV people into this field. They acquire vocal techniques, actively showcase their voices, and adapt their career paths as needed. Social support is crucial for their continued employment, as well as disclosing their BLV identities as appropriate. Observations reveal that BLV people utilize text processing tools, Screen Reader(SR) speed control, and keyboard shortcuts to transform an invisible script into a coherent and emotionally nuanced voice recording. We investigate how BLV people harness their potential through intensive voice acting while listening to SR, and proficient keyboard skills for software access. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Accessibility Assistive Information and Communication Technologies Empirical study that tells us about people Individuals with Disabilities Qualitative Methods

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：