检索结果-内蒙古大学图书馆

A CPK-Based Identity Authentication Scheme for IoT

Computer Systems Science & engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public Key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： Power Internet of Things terminal equipment identity authentication CPK PUF

来源：评论

学校读者我要写书评

暂无评论

Extended Abstract of Candidate Test Set Reduction for Adaptive Random Testing: An Overheads Reduction Technique

Extended Abstract of Candidate Test Set Reduction for Adapti...

引用

IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Rubing Huang Haibo Chen Weifeng Sun Dave Towey Faculty of Information Technology Macau University of Science and Technology Taipa Macau China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang Jiangsu China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China

This document 1 is an extended abstract of a Science of Computer Programming paper, "Candidate Test Set Reduction for Adaptive Random Testing: An Overheads Reduction Technique," presented as a J1C2 (Journal publication first, Conference presentation following) at the 30th IEEE International Conference on software Analysis, Evolution and Reengineering (Saner 2023).The paper presents a candidate set reduction strategy to enhance the Fixed-Sized-Candidate-Set version of Adaptive Random Testing (FSCS-ART). The proposed method reduces the number of randomly-generated candidate test cases by retaining valuable, unused candidates from previous iterations. As the computational costs associated with a stored/retained candidate are less than the costs associated with a randomly-generating one, the overall computational overheads of FSCS-ART are reduced. The reported experimental studies show that the proposed method has a comparable failure-detection effectiveness to FSCS-ART, but less computational overheads.

关键词： Costs Programming software Power capacitors Computational efficiency Testing

来源：评论

学校读者我要写书评

暂无评论

Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation

arXiv

引用

arXiv 2022年

作者： Liu, Kang Xue, Feng He, Xiangnan Guo, Dan Hong, Richang The School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Hefei China The School of Software Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Hefei China The University of Science and Technology of China Anhui Hefei China

Graph Convolution Networks (GCNs), with their efficient ability to capture high-order connectivity in graphs, have been widely applied in recommender systems. Stacking multiple neighbor aggregation is the major operation in GCNs. It implicitly captures popularity features because the number of neighbor nodes reflects the popularity of a node. However, existing GCN-based methods ignore a universal problem: users’ sensitivity to item popularity is differentiated, but the neighbor aggregations in GCNs actually fix this sensitivity through Graph Laplacian Normalization, leading to suboptimal personalization. In this work, we propose to model multi-grained popularity features and jointly learn them together with high-order connectivity, to match the differentiation of user preferences exhibited in popularity features. Specifically, we develop a Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering model, short for JMP-GCF, which uses a popularity-aware embedding generation to construct multi-grained popularity features, and uses the idea of joint learning to capture the signals within and between different granularities of popularity features that are relevant for modeling user preferences. Additionally, we propose a multistage stacked training strategy to speed up model convergence. We conduct extensive experiments on three public datasets to show the state-of-the-art performance of JMP-GCF. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Tamper-Proof Storage of User Movement Logs in Smart Parks 7

Tamper-Proof Storage of User Movement Logs in Smart Parks

引用

7th International Conference on Communication, Image and Signal Processing, CCISP 2022

作者： Niu, Yiru Jiang, Hong Huang, Xiaobing Zhao, Qi Xiang, Hong Xia, Xiaofeng Zhao, Yue Science and Technology on Communication Security Laboratory No.30 Research Institute of China Electronics Technology Group Corporation Chengdu610041 China No.30 Research Institute of China Electronics Technology Group Corporation Chengdu610041 China Qianghua Times Technology Co Ltd. Chengdu610041 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

With the introduction of the smart city concept and the rapid development of various emerging technologies such as cloud computing, Internet of Things, big data and artificial intelligence, traditional parks have also ushered in new opportunities and are developing in a smarter direction. Since the park is an open area with frequent movement of people and equipment, it is prone to security problems. At this point, logs will be a good aid for incident accountability. In this paper, we mainly use Merkle hash tree and distributed storage for secure storage of logs. Meanwhile, to ensure the non-repudiation of logs, the signatures of users and log-generating devices are required when generating logs. After analysis, the scheme proposed in this paper can achieve tamper-proof and traceability of logs. © 2022 IEEE.

关键词： Parks

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation and Machine Learning based Thermal Modeling of Tilted Active Tiles in data Centers 2020

Performance Evaluation and Machine Learning based Thermal Mo...

引用

5th International Conference on Machine Learning Technologies, ICMLT 2020

作者： Rasheed, Zeeshan Xiong, Wei Cong, Gaoxiang Niu, Hongxun Wan, Jianxiong Wang, Yongsheng Li, Lixiao Inner Mongolia University of Technology and Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service China

ISBN: (纸本)9781450377645

Thermal management system of data center continuously face a lot of challenges, because data center industry has seen a boom growth in power density. In this paper we proposed the Tilted Active Tiles (TATs) to improve the local cold air supply and prevent the air flow blow over the rack. In traditional active tiles, fans are placed horizontally which cause the airflow blows over the rack, rather than into, the racks. To solve this issue, we adjusted the angle of the active tile to direct the airflow into the rack. We further introduced ANN based thermal models to predict the thermal performance of TATs. To train the ANN models, we adopted the data set obtained from a data center of Inner Mongolia Meteorology Information Center. The prediction accuracy of the model was extensively compared and analyzed, and the prediction accuracy and overhead of different neural network structures, i.e., BP and LSTM, were evaluated. Experimental results show that the rack with blanking panels has better thermal performance, and the temperature distribution at bottom, middle and top of the rack were same under smaller PWM. Thermal efficiency model was established by BP and LSTM, in this experiment single output model and multi output model were analyzed. The single output model can predict the temperature at different heights on the rack. In single output model the predicted effect of BP model is better than LSTM. The average prediction error is 0.57. The multi-output model can only predict the temperature at a fixed height of the rack. In multi output model LSTM model is better than BP. LSTM prediction error is less than BP. The average prediction error is 0.07. © 2020 ACM.

关键词： Thermoanalysis

来源：评论

学校读者我要写书评

暂无评论

Like an Ophthalmologist: Dynamic Selection Driven Multi-View Learning for Diabetic Retinopathy Grading 39

Like an Ophthalmologist: Dynamic Selection Driven Multi-View...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Luo, Xiaoling Xu, Qihao Wu, Huisi Liu, Chengliang Lai, Zhihui Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different from the previous single-view DR grading methods, we design a dynamic selection-driven multi-view DR grading method to fit clinical scenarios better. Since lesion information plays a key role in DR diagnosis, previous methods usually boost the model performance by enhancing the lesion feature. However, during the actual diagnosis, ophthalmologists not only focus on the crucial parts, but also exclude irrelevant features to ensure the accuracy of judgment. To this end, we introduce the idea of dynamic selection and design a series of selection mechanisms from fine granularity to coarse granularity. In this work, we first introduce an Ophthalmic Image Reader (OIR) agent to provide the model with pixel-level prompts of suspected lesion areas. Moreover, we design a Multi-View Token Selection Module (MVTSM) that prunes redundant feature tokens and dynamically selects key information. In the final decision stage, we dynamically fuse multi-view features through the novel Multi-View Mixture of Experts Module (MVMoEM), to enhance key views and reduce the impact of conflicting views. Extensive experiments on a large multi-view fundus image dataset with 34,452 images prove that our method performs favorably against state-of-the-art models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

Entropy-Based Model Generalization for Sleep Stage Classification 13

Entropy-Based Model Generalization for Sleep Stage Classific...

引用

13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Kim, Dongyoung Na, Younghoon Woo, Yunhee Kim, Dong-Kyu Lee, Jeong-Gun Hallym University Dept. of Computer Engineering Chuncheon Korea Republic of Hallym University Division of Software Chuncheon Korea Republic of Chuncheon Sacred Heart Hospital Hallym University College of Medicine Department of Otorhinolaryngology-Head and Neck Surgery Chuncheon Korea Republic of Institute of New Frontier Research and Division of Big Data and Artificial Intelligence Chuncheon Sacred Heart Hospital Chuncheon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

The most recent researches on sleep stage classification have been focused on a model architecture to attain high classification accuracy. Sleep stage classification is performed by human sleep experts and consequently there are some inconsistencies of labels among the scoring experts. To improve robustness of the sleep stage classification model, it is essential to consider those noisy labels which have been commonly mentioned in typical image classification problems as well. To partly solve the problem, we employ 'entropy minimization' in a loss function. By including the entropy, we can make a generalized model with regard to the label noises. To validate the effectiveness of the use of entropy on model generalization, we use two different datasets gathered from two institutions. We train a model only using a dataset obtained from one institution. Then we test the model using the dataset of another institution to investigate the accuracy improvement by the generalization. © 2022 IEEE.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Evaluation of OSA Patient Sleep Stage Classification Performance Using a Multi-Channel PSG dataset

Evaluation of OSA Patient Sleep Stage Classification Perform...

引用

2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022

作者： Na, Younghoon Kim, Dongyoung Kim, Dong-Kyu Lee, Jeong-Gun Division of Software Hallym University Chuncheon Korea Republic of Dept. of Computer Engineering Hallym University Chuncheon Korea Republic of Department of Otorhinolaryngology-Head and Neck Surgery Chuncheon Sacred Heart Hospital Hallym University College of Medicine Chuncheon Korea Republic of Institute of New Frontier Research Division of Big Data and Artificial Intelligence Chuncheon Sacred Heart Hospital Chuncheon Korea Republic of

ISBN: (数字)9781665464345

ISBN: (纸本)9781665464345

In this paper, we conduct a comparative analysis of sleep stage classification for patients having different levels of obstructive sleep apnea (OSA). For the analysis, we use 10 bio-signal channels: 4 EEG (Electroencephalogram) channels (F3-M2, F4-M1, C3-M2, and C4-M1), 2 EOG (Electrooculogram) channels (E1-M2 and E2-M1), and 4 other bio-channels (EMG, ECG, Flow, and Abdomen). In this work, in particular, we consider OSA severity for training and testing. Then, we investigated the detailed impacts of the OSA severity on the accuracy performance of a modern deep learning model with single channel and multiple channels. © 2022 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Construction Cost Index Forecasting: A Multi-feature Fusion Approach

arXiv

引用

arXiv 2021年

作者： Zhan, Tianxiang He, Yuanpeng Xiao, Fuyuan College of Computer and Information Science College of Software Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

The construction cost index is an important indicator of the construction industry. Predicting CCI has important practical significance. This paper combines information fusion with machine learning, and proposes a multi-feature fusion (MFF) module for time series forecasting. Compared with the convolution module, the MFF module is a module that extracts certain features. Experiments have proved that the combination of MFF module and multi-layer perceptron has a relatively good prediction effect. The MFF neural network model has high prediction accuracy and efficient prediction efficiency. At the same time, MFF continues to improve the potential of prediction accuracy, which is a study of continuous attention. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Model Predictive Control for Carbon-Neutral data Centers

Model Predictive Control for Carbon-Neutral Data Centers

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Guanyu Ren Jianxiong Wan Leixiao Li Chuyi Liu Xiaolei Wang Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China China

As one of the major carbon producers, data centers produce around 1% of global carbon emissions per year. Researchers are making significant effort to reduce the data center carbon emissions. However, the current carbon-neutral data center solutions seldom take the carbon quotas into account, nor do they integrate new emission reduction technologies like Carbon Capture (CC) and Power-to-Gas (P2G), etc. To bridge this gap, in this paper a novel carbon-neutral data center architecture is proposed based on which a holistic cost minimizing problem is formulated. We use the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) neural network to construct highly accurate prediction models, and develop a Model Predictive Control (MPC) algorithm for energy and carbon management. Finally, simulations on real-world data demonstrate that our approach reduces up to 19.19% overall cost compared with traditional solutions.

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：