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： Chen, Yu Dai, Hua Yang, Geng Chen, Yanli Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210003 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210003 China

ISBN: (纸本)9781665405270

Using the trajectory data of moving objects to analyze and study the infection mode of viruses or germs has practical application value. The definition of infection pattern in existing works only considers one-to-one infection mode rather than many-to-one mode, and thus some infection events could be ignored. This paper presents multi-source infection pattern mining algorithms oriented to moving objects for the first time. The multi-source infection event (MSIE) is defined. On the basis of MSIE, the multi-source infection pattern mining algorithm (MIPM) is proposed, which uses sliding window mechanism. The sliding window is used to record the set of candidate infection source that may infect each normal object at each time point, and then the determined infection events are mined. To improve performance, an optimized mining algorithm (MIPM+) based on the R-tree index is proposed, which can reduce the number of objects to be detected at each time point. The experimental results show that our proposed multi-source infection pattern mining algorithms can mine more potential infection events. © 2022 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Customized scheduling for shared bus with deadlines

Customized scheduling for shared bus with deadlines

引用

作者： Jin, Yong Xu, Jia Xu, Lijie Liu, Linfeng Xiao, Fu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science & Engineering Changshu Institute of Technology Jiangsu Changshu China

Public transportation system is one of the most effective ways to conserve energy and reduce carbon emissions. However, the traditional public transportation system does not provide customized service and cannot guarantee the arrival time to destination. To address these issues, we formulate the minimum shared bus scheduling problem to minimize the number of shared buses such that all orders can be completed under constraints of deadlines and capacity of shared bus. We propose the approximation algorithms, S-MBSA for the shared bus with strong endurance and E-MBSA for the large-scale order scenario, to solve the minimum shared bus scheduling problem. We further formulate the constrained maximum revenue shared bus scheduling problem to maximize the revenue under the limited number of shared buses, and propose an approximation algorithm, CMRBSA, to find the shared bus route schedules. Through the extensive simulations, we demonstrate the significant superiority of S-MBSA and E-MBSA in terms of number of shared buses. Furthermore, CMRBSA outperforms the benchmark algorithms significantly in terms of revenue. © 2022 John Wiley & Sons Ltd.

关键词： Scheduling

来源：评论

学校读者我要写书评

暂无评论

Recognition of Disaster Images Based on Self-Supervised Learning 7

Recognition of Disaster Images Based on Self-Supervised Lear...

引用

7th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2022

作者： Wang, Linyong Wu, Ruiqi Li, Cen Zou, Zhiqiang Nanjing University of Posts and Telecommunications NanJing China College of Computer Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing NanJing China

ISBN: (纸本)9781665487115

Existing supervised deep learning model requires large amounts of labeled training data to learn new tasks. This is a limitation for many practical applications in disaster areas as well as in many other fields such as medical domain, where numerous labour and time are required to label datasets. According to our literature survey, there is few self-supervised learning model with high accuracy for image recognition about natural disasters. This paper proposed a self-supervised learning model for disaster image recognition based on MoCo model, called MoCo-Q, including image preprocessing, model training and transfer learning after fine-Tuning. After being trained by learning a couple of features of disaster images without artificial labels, the trained neural network is applied to downstream tasks, which achieves the recognition of a large number of disaster images at a high accuracy without the manual intervention and labeling. With the implement of the MoCo-Q, images of natural disasters can be quickly recognized without manual labeling. The work can provide scientific decision-making support for the government and it is of great significance for natural disaster assessment and disaster relief. Finally, experiments on real-world natural disaster datasets, CrisisMMD and ASONAM17, are performed, and we compared with the state-of-The-Art supervised method (ResNet50) that uses same datasets. The result presents that the accuracy of the MoCo-Q (91.56%) is 5.71% higher than that of the supervised learning model (85.85%). © 2022 IEEE.

关键词： Training Image recognition Neural networks Transfer learning Supervised learning Training data Manuals

来源：评论

学校读者我要写书评

暂无评论

BDLR:lncRNA identification using ensemble learning

引用

BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab 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 Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

来源：评论

学校读者我要写书评

暂无评论

Learning Disentangled Latent Factors for Individual Treatment Effect Estimation Using Variational Generative Adversarial Nets 25

Learning Disentangled Latent Factors for Individual Treatmen...

引用

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

作者： Bao, Qingsen Mao, Zeyong Chen, Lei Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781665405270

Estimating individual treatment effect (ITE) is a challenging task due to the need for individual potential outcomes to be learned from biased data and counterfactuals are inherently unobservable. Some researchers propose to use generative adversarial approaches to infer the counterfactual outcomes based on the distribution of factual outcomes. However, these methods assume that complete confounding factors are observed, and simply treat all observed variables as confounding factors, ignoring identification of possible instrumental factors and adjustment factors, which will bring large deviation to ITE estimation when facing biased data. To address these issues, we propose a novel Variational Generative Adversarial Nets for ITE estimation by designing the collaborative learning strategy with Variational AutoEncoder (VAE) and Generative Adversarial Nets (GAN). Specifically, we employ VAE to infer the latent representations of observed variables to access complete latent factors while using GAN to infer unseen counterfactual outcomes and guide VAE for disentangling these latent factors into three sets corresponding to the instrumental, confounding, and adjustment factors. Then the disentangled latent confounding factors can be used to further control data bias using an adaptive weighting scheme. Extensive experiments on real and synthetic data demonstrate learning disentangled latent factors for ITE estimation is effective, and our method has excellent performance even with high data bias. © 2022 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Community Detection Based on Deep Network Embedding with Dual Self-supervised Training 3rd

Community Detection Based on Deep Network Embedding with Dua...

引用

3rd International Conference on big data and security, ICBDS 2021

作者： Chen, Yunfang Mao, Haotian Wang, Li Zhang, Wei School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

ISBN: (纸本)9789811908514

We propose a community discovery method based on deep auto-encoding (DGAE_DST). Firstly, we use the pre-trained two-layer neural network and k-means algorithm to initialize the centroid vector, and then use the DNN module and the GCN module to capture the structure features and attributes respectively. The attribute information is fed to the GCN layer to integrate the structure information and attributes. Finally, DNN and GCN respectively output the reconstructed adjacency matrix and feature matrix, and minimize their loss from the original matrix. The experimental results on four datasets show that the DGAE_DST method has higher stability and accuracy. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Complex networks

来源：评论

学校读者我要写书评

暂无评论

An adaptive flow table adjustment algorithm for SDN 21

An adaptive flow table adjustment algorithm for SDN

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xu, Xiaolong Hu, Liuyun Lin, Haowei Fan, Zexuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728120584

SDN (Software-defined networking) are important for current network systems, such as cloud systems. The characteristics of flow in SDN and the impact of flow table entries and controllers on data packet transmission are analyzed. We propose an adaptive flow table adjustment algorithm for SDN called CFC (Combine flow table and controller). It is important for CFC to monitor the network traffic in real time. According to the proportion of active flow entries in the OpenFlow switch, the value of idle-timeout will be set dynamically based on the characteristics of different data flows. When the switch forwards packets, it will minimize the calculation load of controller, and allocate a reasonable value of idle-timeout to the idle flow table entries. So it can delete discarded flow table entries in time and improve the hit rate of data packets. The experiments show that the utilization rate of the flow table resource is enhanced. With the improvement of data packets' forwarding rate, the average throughput of the network has also increased. At the same time, it can also cope with the burst traffic in SDN and ensure them to be forwarded normally and effectively. © 2019 IEEE.

关键词： Controllers

来源：评论

学校读者我要写书评

暂无评论

TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud 27

TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud

引用

27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

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

ISBN: (纸本)9781665408783

In order to select a composition scheme that meets user's needs and high performance from large-scale web services in the edge cloud, this paper proposes a trusted service composition optimization scheme called TSC-ECFA for edge cloud, which applies the predation strategy to the firefly algorithm (FA) and divides the services with the same input and output into one category, reducing the number of combinations to search for feasible solutions. The cotangent chaos theory is used to generate the initial firefly population and disturb the location of the individual to improve the overall search efficiency of FA. This paper also improves the step factor and attractiveness formula of FA to give full play to the detection ability of the step factor in the early stage of the algorithm, and balance the local search and the global search. Considering that the QoS attribute values of the service are vulnerable to be tampered with, the blockchain is used to store the QoS attribute values to ensure the tamper-proof and reliability of the QoS attribute value. Finally, simulation experiments compare the number of iterations and optimization of the three algorithms. The experimental results show that the comprehensive performance of TSC-ECFA is better than other algorithms. © 2021 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

APDPk-means: A new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation 21

APDPk-means: A new differential privacy clustering algorithm...

引用

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

ISBN: (纸本)9781728120584

How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering algorithms focus on optimizing the selection of initial centroids. However, the traditional privacy budget allocation has the problem that the random noise becomes too large as the number of iterations increases, which will reduce the performance of data clustering. To solve the problem, we improved the way of privacy budget allocation in differentially private clustering algorithm DPk-means, and proposed APDPk-means, a new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation. APDPk-means decomposes the total privacy budget into a decreasing arithmetic progression, allocating the privacy budgets from large to small in the iterative process, so as to ensure the rapid convergence in early iteration. The experiment results show that compared with the other differentially private k-means algorithms, APDPk-means has better performance in availability and quality of the clustering result under the same level of privacy protection. © 2019 IEEE.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Design Guidance for Lightweight Object Detection Models 3rd

Design Guidance for Lightweight Object Detection Models

引用

3rd International Conference on big data and security, ICBDS 2021

作者： Wang, Rui Wang, Xueli Chen, Yunfang Zhang, Wei School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

ISBN: (纸本)9789811908514

The lightweight target detection model is deployed in an environment with limited computing power and power consumption, which is widely used in many fields. Most of the current lightweight technologies only focus on a few steps of the model implementation and lack a global perspective. Therefore, this paper proposes a general lightweight model implementation framework, including network construction indicators, lightweight backbone network design, and model optimization. By analyzing the complexity indicator of network structure, the factors that affect network performance such as depth and width are summarized. On this basis, combine the One-Shot Aggregation (OSA) idea and Cross-Stage Partial Network (CSPNet) transformation to construct a general lightweight detection network CSPOSA. Further specific optimization strategies are proposed to prune the network structure and training process. For the network structure, the width and depth of the network are adjusted, the amount of parameters of the model is compressed. The training process is divided into the first, middle and last three stages to improve the detection performance of the model without adding extra computation. Taking embedded platform helmet detection as the experimental scene, the parameter amount of the realized model is 1/10 of the mainstream models YOLOv3 and YOLOv4, and the detection accuracy is similar, so it is more suitable for deployment on devices with limited computing power. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Computing power

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：