检索结果-内蒙古大学图书馆

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Sun, Wei Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing China

ISBN: (纸本)9781665405270

Effective workload forecasting can provide a reference for resource scheduling in cloud data centers. Compared with the normal single data center, the multi-data center has a more complicated architecture design and provides more diverse computing services. The traditional load forecasting models need too much manual intervention to set up the parameters, meanwhile, the emerging neural network methods are not sensitive to the prediction scale and cannot capture the long-term associations between input features. To address these challenges, we propose a hybrid model named Attentions-based LSTM Encoder-Decoder network and Autoregressive model (ALEDAR), which combines neural network and statistical learning methods, to analyze the linear and nonlinear characteristics of the load sequence over time in a multi-cloud data center environment. ALEDAR uses a dual attention-based Encoder-Decoder architecture to extract the relationships between historical workload data, the design can also avoid the deterioration of prediction effect caused by long-range data scale, the output layer is composed of a three-layer perceptron. Moreover, ALEDAR employs an autoregressive module to capture the linear trend of the load sequence and eliminate scale insensitivity of input and output data. The experimental results show that our approach is adaptive and can improve the performance of host workload prediction in both single cloud data center and multi-cloud data center environments. We evaluate the performance of the proposed algorithm on real-world data sets and observe consistent improvement of 9.7% - 34.2% over the state-of-the-art baselines. © 2022 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

A New Semi-supervised Approach for Network Encrypted Traffic Clustering and Classification 25

A New Semi-supervised Approach for Network Encrypted Traffic...

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Lin, Kunda Xu, Xiaolong Jiang, Yu School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781665405270

Encrypted network traffic classification is an essential task in modern communications, which is used in a wide range of applications, such as network resource allocation, QoS (Quality of Service), malicious detection, etc. With the continuous evolution of network technology, approaches used to classify network encrypted traffic have become increasingly complex, and model training relies heavily on a large amount of labeled data. However, the acquisition of correct and massive labeled data of network traffic in the real environment remains a major challenge in this field. On the opposite, unlabeled traffic is extremely easy to obtain in a network. Therefore, the effective use of unlabeled data is of great significance to the development of modern communications. In this paper, we propose the Sauce model, which can effectively use unlabeled information to obtain high-quality clustering space. It is composed of Aux (auxiliary network) and AE (auto encoder), where Aux is designed for collaborative training with AE, affecting the distribution of samples in the latent space generated by AE. Sauce uses t-SNE (t- distributed stochastic neighbor embedding) to perform secondary dimensionality reduction on the latent code, and then uses a clustering algorithm for cluster analysis. Besides, sauce applies the self-training technique, replacing the real labeled data with pseudo-labeled data to reduce the reliance on labeled data and improve the utilization of unannotated data. We conduct experiments on two real network datasets. The experiments show that Sauce can achieve clustering accuracies of 98.4% (VPN dataset) and 98.0% (TOR dataset), outperforming other unsupervised or semi-supervised learning methods. © 2022 IEEE.

关键词： Cluster analysis

来源：评论

学校读者我要写书评

暂无评论

CABAC: A content-driven attribute-based access control model for big data 5th

CABAC: A content-driven attribute-based access control model...

引用

5th International Conference on Artificial Intelligence and security, ICAIS 2019

作者： Ma, Ke Yang, Geng Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9783030242701

In recent years, attribute-based access control (ABAC) models have been widely used in big data and cloud computing. However, with the growing importance of data content, using data content to assist authorization for access controls has become more common. In this paper, we propose a dynamic content-driven attribute-based access control model (CABAC) for large-scale unstructured data. CABAC is a fine-grained access control model that use two-layer authorization to balance efficiency and accuracy. The first-layer authorization uses attributes to grant users basic authority and the second-layer authorization uses data content to grant broader authority over "related" data. Experimental results show that CABAC has acceptable efficiency and it can expand the authority of users without reducing security. © Springer Nature Switzerland AG 2019.

关键词： big data

来源：评论

学校读者我要写书评

暂无评论

Topic modeling for noisy short texts with multiple relations 30

Topic modeling for noisy short texts with multiple relations

引用

30th International Conference on Software Engineering and Knowledge Engineering, SEKE 2018

作者： Liu, Chiyu Liu, Zheng Li, Tao Xia, Bin Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)1891706446

Understanding contents in social networks by inferring high-quality latent topics from short texts is a significant task in social analysis, which is challenging because social network contents are usually extremely short, noisy and full of informal vocabularies. Due to the lack of sufficient word co-occurrence instances, well-known topic modeling methods such as LDA and LSA cannot uncover high-quality topic structures. Existing research works seek to pool short texts from social networks into pseudo documents or utilize the explicit relations among these short texts such as hashtags in tweets to make classic topic modeling methods work. In this paper, we explore this problem by proposing a topic model for noisy short texts with multiple relations called MRTM (Multiple Relational Topic Modeling). MRTM exploits both explicit and implicit relations by introducing a document-Attribute distribution and a two-step random sampling strategy. Extensive experiments, compared with stateof-the-Art topic modeling approaches, demonstrate that MRTM can alleviate the word co-occurrence sparsity and uncover highquality latent topics from noisy short texts. © 2018 Universitat zu Koln. All rights reserved.

关键词： Quality control

来源：评论

学校读者我要写书评

暂无评论

Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods

引用

Computer Modeling in Engineering & Sciences 2021年第10期129卷 31-45页

作者： Lejun Gong Xingxing Zhang Li Zhang Zhihong Gao Jiangsu Key Laboratory of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China

Novel coronavirus disease 2019(COVID-19)is an ongoing health *** studies are related to ***,its molecular mechanism remains *** rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational *** paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine *** method obtains seed genes based on prior *** genes are mined from biomedical *** candidate genes are scored by machine learning based on the similarities measured between the seed and candidate ***,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of *** results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.

关键词： COVID-19 SARS-CoV-2 computational method bioinformatics genotype machine learning

来源：评论

学校读者我要写书评

暂无评论

SR-AFU: super-resolution network using adaptive frequency component upsampling and multi-resolution features

引用

Frontiers of Computer Science 2023年第1期17卷 123-132页

作者： Ke-Jia CHEN Mingyu WU Yibo ZHANG Zhiwei CHEN School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China

Image super-resolution (SR) is one of the classic computer vision tasks. This paper proposes a super-resolution network based on adaptive frequency component upsampling, named SR-AFU. The network is composed of multiple cascaded dilated convolution residual blocks (CDCRB) to extract multi-resolution features representing image semantics, and multiple multi-size convolutional upsampling blocks (MCUB) to adaptively upsample different frequency components using CDCRB features. The paper also defines a new loss function based on the discrete wavelet transform, making the reconstructed SR images closer to human perception. Experiments on the benchmark datasets show that SR-AFU has higher peak signal to noise ratio (PSNR), significantly faster training speed and more realistic visual effects compared with the existing methods.

关键词： super-resolution multi-resolution features adaptive frequency upsampling wavelet transformation

来源：评论

学校读者我要写书评

暂无评论

Community-based message forwarding in mobile social networks

Community-based message forwarding in mobile social networks

引用

2019 International Conference on Advances in Computer Technology, Information Science and Communications, CTISC 2019

作者： Chen, Zhiming Xiang, Yang School of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9789897583575

With the popularity of various smart devices and the application of sensor network technology, message transmission using mobile devices is becoming widespread. This paper focuses on the forwarding in mobile social network (MSN). The MSN is a special Delay Tolerant Network (DTN) consisting of mobile nodes. In MSN, nodes move and share information with each other through carried short-range wireless communication devices. Mobile nodes in the MSN typically access some building areas more frequently, such as schools, companies, or apartments, while visiting other areas, such as the roads between buildings, less frequently. The building areas that nodes frequently visit are called communities. To increase delivery ratio and reduce transmission time in MSN, this paper proposes a novel zero-knowledge multi-copy routing algorithm, Mixed Message Forwarding (MMF) which exploits and improves the metric, namely centrality. Centrality reflects the importance of a node in the network. MMF improves copy diffusion by using different directions of node movement as well. Special facilities called boundary boxes are added to the network scenario. Boundary boxes are special throw boxes. Throw boxes are relays with large storage space and fixed position. MMF is designed and evaluated, which utilizes the aforementioned boundary boxes to reduce transmission delay. The simulation results show that the MMF can improve the delivery ratio and reduce the transmission delay, compared with other algorithms. © 2019 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Delay tolerant networks

来源：评论

学校读者我要写书评

暂无评论

Classifying Galaxy Morphologies with Few-shot Learning

引用

Research in Astronomy and Astrophysics 2022年第5期22卷 9-20页

作者： Zhirui Zhang Zhiqiang Zou Nan Li Yanli Chen College of Computer Nanjing University of Posts and Telecommunications Nanjing 210023.China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

The taxonomy of galaxy morphology is critical in astrophysics as the morphological properties are powerful tracers of galaxy *** the upcoming Large-scale Imaging Surveys,billions of galaxy images challenge astronomers to accomplish the classification task by applying traditional methods or human ***,machine learning,in particular supervised deep learning,has been widely employed to classify galaxy morphologies recently due to its exceptional automation,efficiency,and ***,supervised deep learning requires extensive training sets,which causes considerable workloads;also,the results are strongly dependent on the characteristics of training sets,which leads to biased outcomes *** this study,we attempt Few-shot Learning to bypass the two *** research adopts the data set from the Galaxy Zoo Challenge Project on Kaggle,and we divide it into five categories according to the corresponding truth *** classifying the above data set utilizing few-shot learning based on Siamese Networks and supervised deep learning based on AlexNet,VGG_16,and ResNet_50 trained with different volumes of training sets separately,we find that few-shot learning achieves the highest accuracy in most cases,and the most significant improvement is 21%compared to AlexNet when the training sets contain 1000 *** addition,to guarantee the accuracy is no less than 90%,few-shot learning needs~6300 images for training,while ResNet_50 requires~13,000 *** the advantages stated above,foreseeably,few-shot learning is suitable for the taxonomy of galaxy morphology and even for identifying rare astrophysical objects,despite limited training sets consisting of observational data only.

关键词： Galaxies-Galaxy morphological classification-Method neural networks

来源：评论

学校读者我要写书评

暂无评论

Point Cloud Compression Method Based on Curvature Grading and Inter-point Compression for Industrial Products 15

Point Cloud Compression Method Based on Curvature Grading an...

引用

15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

作者： Wang, Lei Huang, Wei Chen, Yiping Chen, Ran Nanjing University of Posts and Telecommunications School of Computer Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350308693

The traditional point cloud compression methods fail to meet the compression requirements of complex surface point cloud files in the industrial field. This paper proposes a point cloud compression method suitable for industrial products with defective and complex surfaces, which compresses the defect and non-defect parts separately using different compression approaches. A curvature-based compression method is designed for the defect points, and an inter-point compression method is developed for the non-defect part, achieving compression of both components of the point cloud. The experimental results show the advantages of the proposed compression method through a comparative analysis of time efficiency, compression ratio, and reconstruction accuracy for industrial point cloud compression. © 2023 IEEE.

关键词： Grading

来源：评论

学校读者我要写书评

暂无评论

Anomaly detection for drinking water quality via deep bilSTM ensemble 18

Anomaly detection for drinking water quality via deep bilSTM...

引用

2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Chen, Xingguo Feng, Fan Wu, Jikai Liu, Wenyu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu Province China

ISBN: (纸本)9781450357647

In this paper, a deep BiLSTM ensemble method was proposed to detect anomaly of drinking water quality. First, a convolutional neural network (CNN) is utilized as a feature extractor in order to process the raw data of water quality. Second, bidirectional Long Short Term Memory (BiLSTM) is employed to handle the time series prediction problem. Then, a linear combination of t-time-step predictions weighted by a discount factor was utilized to ensemble the final output of event. Finally, cost-sensitive learning combined with Adam optimization was applied to learn the model according to the imbalance property of the event label. © 2018 Copyright held by the owner/author(s).

关键词： Long short-term memory

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：