检索结果-内蒙古大学图书馆

On the parameterized complexity of minimum/maximum degree vertex deletion on several special graphs

Frontiers of Computer Science 2023年第4期17卷 97-107页

作者： Jia LI Wenjun LI Yongjie YANG Xueying YANG School of Information Engineering Hunan Industry PolytechnicChangsha 410036China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410015China Chair of Economic Theory Saarland UniversitySaarbrücken 66123Germany

In the minimum degree vertex deletion problem,we are given a graph,a distinguished vertex in the graph,and an integer κ,and the question is whether we can delete at most κ vertices from the graph so that the distinguished vertex has the unique minimum *** maximum degree vertex deletion problem is defined analogously but here we want the distinguished vertex to have the unique maximum *** is known that both problems areΨ-hard and fixed-parameter intractable with respect to some natural *** this paper,we study the(parameterized)complexity of these two problems restricted to split graphs,p-degenerate graphs,and planar *** study provides a comprehensive complexity landscape of the two problems restricted to these special graphs.

关键词： minimum degree maximum degree vertex deletion split graphs planar graphs parameterized complexity

来源：评论

学校读者我要写书评

暂无评论

Research on Backward Cloud Transformation Algorithm Based on Adding Cloud Drops 9

Research on Backward Cloud Transformation Algorithm Based on...

引用

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

作者： Xu, Changlin Kong, Lingzhuo School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ning Xia Province Yinchuan750021 China

ISBN: (纸本)9781643684703

As a tool for studying uncertain information, cloud model is of great significance in uncertain artificial intelligence and data mining. Among them, the backward cloud transformation method is one of the important algorithms of cloud model, which can realize the conversion from quantitative data to qualitative concepts. In this paper, a dynamic incremental backward cloud transformation algorithm is proposed to solve the problem that the estimated value of hyper entropy is imaginary when the backward cloud transformation method is used to calculate hyper entropy. First, according to the formation characteristics of cloud drops, the generated samples and the original samples are fused as new samples by dynamically and randomly generating samples, and then the hyper entropy is estimated until the estimated value of hyper entropy is real. Secondly, the stability and convergence of the algorithm proposed in this paper are analyzed through simulation experiments. The experimental results show that the new dynamic incremental backward cloud transformation algorithm solves the problem that the hyper entropy estimation value is imaginary while the estimation error is small and the stability is good. Finally, the algorithm is applied to brain CT segmentation, and the results show that the algorithm is effective and practical. © 2023 The authors and IOS Press.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Feature detection method for weak signal of stochastic resonance based on PSO

Feature detection method for weak signal of stochastic reson...

引用

第八届国际振动工程会议

作者： JINGJINGWANG SHAOJUANMA School of Mathematics and Information Science North Minzu University The Key Laboratory of Intelligent Information and Big Data Processing of NingXia

In order to improve the effect of stochastic resonance theory on weak signal detection, a new coupled stochastic resonance system based on self mutation PSO parameters is proposed. The signal-to-noise ratio(SNR) is used as the measurement index, and the self mutation PSO algorithm is used to optimize the parameters. In the case of less sampling points, numerical simulation is carried out to realize the detection of single frequency, multi frequency sinusoidal signal and single pulse signal. Compared with the classical bistable system, the proposed coupling system is proved to be effective and feasible, and the detection effect is ideal. Finally, the system is applied to the bearing fault detection, and the effect is better than the classical bistable system.

关键词： Stochastic resonance coupled system adaptive mutation PSO weak signal detection fault diagnosis

来源：评论

学校读者我要写书评

暂无评论

HSCS:a hybrid shared cache scheduling scheme for multiprogrammed workloads

引用

Frontiers of Computer Science 2018年第6期12卷 1090-1104页

作者： Jingyu ZHANG Chentao WU Dingyu YANG Yuanyi CHEN Xiaodong MENG Liting XU Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410004China School of Electronics and Information Shanghai Dianji UniversityShanghai 200240China

The traditional dynamic random-access memory (DRAM) storage medium can be integrated on chips via modern emerging 3D-stacking technology to architect a DRAM shared cache in multicore systems. Compared with static random-access memory (SRAM), DRAM is larger but slower. In the existing research, a lot of work has been devoted to improving the workload performance using SRAM and stacked DRAM together in shared cache systems, ranging from SRAM structure improvement to optimizing cache tags and data access. However, little attention has been paid to designing a shared cache scheduling scheme for multiprogrammed workloads with different memory footprints in multicore systems. Motivated by this, we propose a hybrid shared cache scheduling scheme that allows a multicore system to utilize SRAM and 3D-stacked DRAM efficiently, thus achieving better workload performance. This scheduling scheme employs (1) a cache monitor, which is used to collect cache statistics; (2) a cache evaluator, which is used to evaluate the cache information during the process of programs being executed; and (3) a cache switcher, which is used to self-adaptively choose SRAM or DRAM shared cache modules. A cache data migration policy is naturally developed to guarantee that the scheduling scheme works correctly. Extensive experiments are conducted to evaluate the workload performance of our proposed scheme. The experimental results showed that our method can improve the multiprogrammed workload performance by up to 25% compared with state-of-the-art methods (including conventional and DRAM cache systems).

关键词： multicore system shared cache workload performance

来源：评论

学校读者我要写书评

暂无评论

Privacy protection oriented on data stream computing

Privacy protection oriented on data stream computing

引用

2022 International Conference on Computer Application and information Security, ICCAIS 2022

作者： He, Renjie Zheng, Xianghan Wang, Gong School of Computer and Big Data Fuzhou University Fujian Fuzhou China The Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fujian Fuzhou China

ISBN: (纸本)9781510663459

data security has always been one of the public concerns, and homomorphic encryption technology, as one of the effective means to ensure data security, has always been a hot topic of academic research. Traditional machine learning is trained in a batch environment. In practice, data owners tend to outsource data and model training tasks to third-party clouds with huge computing resources, but data outsourcing carries the risk of privacy leakage. Therefore, in order to prevent this situation, an effective method is to encrypt the data before the outsourcing, and the third-party cloud trains the machine model of the ciphertext data. This method has special requirements for encryption algorithms which should support computation directly on the ciphertext data. This paper presents a novel data flow computing privacy protection framework based on the homomorphic encryption algorithm CKKS and the flow data processing engine Flink, called SDPPF (Streaming data Privacy Protection on Flink). The proposed framework supports the functions of data stream encryption, decryption and ciphertext computing, and expands the functions of vector point multiplication and array sum operation on the basic simultaneous operation supported by the original CKKS algorithm. This paper also selects a classical machine learning algorithm: KNN algorithm, combined with the homomorphic encryption algorithm CKKS to realize the privacy protection machine learning algorithm. © 2023 SPIE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

A video archive management method based on blockchain technology 11

A video archive management method based on blockchain techno...

引用

Optoelectronic Imaging and Multimedia Technology XI 2024

作者： Wu, Rongteng College of Computer and Big Data Minjiang University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

ISBN: (纸本)9781510682061

Video archives play a crucial role in various industries, from media and entertainment to security and surveillance. Managing these archives efficiently and securely is a growing challenge due to the increasing volume of video data being generated. In this paper, a video archive management method is presented to solve security and sensibility of the video files on blockchain. By encrypting video files and generating cryptographic hashes, the presented method can protect the integrity of the data and prevent unauthorized modifications. The method based on blockchain technology offers a compelling solution to the challenges faced in managing video archives. By combining the inherent security and transparency of blockchain with innovative data management algorithms, organizations can ensure the integrity, security, and accessibility of their video archives. © 2024 SPIE.

关键词： data integrity

来源：评论

学校读者我要写书评

暂无评论

A big data framework for short-term load forecasting using heterogenous data

A big data framework for short-term load forecasting using h...

引用

2020 International Conference on Virtual Reality and intelligent Systems, ICVRIS 2020

作者： Zhao, Haibo Li, Xiangjun Ren, Yancheng Yan'An University School of Physics and Electronic Information Shaanxi Yan'an716000 China Shaanxi Key Laboratory of Intelligent Processing of Big Energy Data Shaanxi Yan'an716000 China

ISBN: (纸本)9781728196367

In order to forecast Short-Term Load Forecast (STLF) for individual electric customers and then intelligently and flexibly utilize renewable energy, a big data framework is proposed that collects the data from smart meters and weather forecasting, pre-processes and loads it into a NoSQL database that is capable to store and further process large volumes of heterogeneous data. Then, a Long Short-Term Memory (LSTM) recurrent neural network is designed and implemented to determine the load profiles and forecast the electricity consumption for residential community for the next 24 hours. The proposed framework is tested with a publicly available smart meter dataset of a residential community, of which LSTM's performance is compared with two benchmark algorithms in terms of Root Mean Square Errors (RMSE) and Mean Absolute Percentage Errors (MAPE). © 2020 IEEE.

关键词： big data

来源：评论

学校读者我要写书评

暂无评论

Software Defect Prediction via Deep Belief Network

引用

Chinese Journal of Electronics 2019年第5期28卷 925-932页

作者： WEI Hua SHAN Chun HU Changzhen ZHANG Yu YU Xiao School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology School of Electrical and Information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture State Key Laboratory in China for Geomechanics and Deep Underground Engineering (Beijing) China University of Mining and Technology School of Computer Science and Technology Shandong University of Technology

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of *** problem of data redundancy exists in software defect data set, which has great influence on prediction *** propose a defect distribution prediction model(Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network(DBN)technology, which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source *** results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine(SVM), Locally linear embedding SVM(LLE-SVM), and Neighborhood preserving embedding SVM(NPE-SVM).

关键词： Defect prediction Software security Deep belief network(DBN)

来源：评论

学校读者我要写书评

暂无评论

Investigation of the Salinity Distribution of the Soil along West Main Canal in ningxia, China

Investigation of the Salinity Distribution of the Soil along...

引用

2021 International Conference on Agriculture Science and Water Resource, ASWR 2021

作者： Wu, Juntao Jing, Hefang Wang, Weihong Guo, Xiaoxiao Guo, Xinxia Research Institute of Numerical Computation and Engineering Applications North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Yinchuan China School of Mathematics and Information Science North Minzu University Yinchuan China

As one of the main irrigation canals in ningxia, West Main Canal plays a very important role in the irrigation of the land nearby the canal, such as farms, meadows, and tree farms. Because of the long-term use of the Yellow River water for irrigation, the soil salinization in this area become more serious. In this paper, the problem of soil salinization along the West Main Canal in ningxia is taken as the research object. 82 sample points in the studied area are chosen and a total number of 164 soil samples are measured for the eight main salt ions and pH value. Correlation analysis and principal component analysis are employed to study the distribution of the soil salinity ions and pH value. In addition, two dimensional interpolation is applied to obtain the spatial distribution of the total saltiness. It can be found that Cl-, SO42-, Ca2+, Na+ and HCO3- are the main salt ions and the main soluble salts are NaCl, KCl, CaSO4, Na2SO4 and CaCl2 in the soil of the studied area. The spatial distribution of the total soil salt is mainly in the patterns of north high and south low, far high and near low, up high and down low. © Published under licence by IOP Publishing Ltd.

关键词： Sodium chloride

来源：评论

学校读者我要写书评

暂无评论

IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

引用

Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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：