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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Xiaolong Ding, Qun Lin, Licheng Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350331684

With the increase of car ownership in China, the problem of difficult parking in cities has become more and more serious. In large parking garage, finding an ideal parking space has become a daily problem for people;especially during the peak usage period of parking garage, a large number of vehicles will be driven into the parking garage, which will bring congestion, and at the same time, the demand of a large number of users in the area will lead to network congestion under 4G network, and users cannot access the network to check and find parking spaces. Therefore, this paper proposes a global optimal navigation mechanism with 5G network based on the above mentioned problems by analyzing the congestion situation during the peak usage of the parking garage. This mechanism uses an optimal parking space selection model and a global optimal scheduling model that can avoid congestion. Based on the model proposed in this paper, we construct a navigation planning system based on mobile, front-end and back-end. With the ultra-low latency, ultrahigh transmission efficiency and reliability of 5G network, the mobile port can select the optimal parking space according to the user's preference, and the back-end can show the parking space and congestion in the parking garage in real time according to the actual situation of the parking garage. Finally, the performance of the model and system proposed in this paper is verified through experiments. © 2023 IEEE.

关键词： 5G mobile communication systems

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Estimating Power Consumption of Containers and Virtual Machines in data Centers

Estimating Power Consumption of Containers and Virtual Machi...

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IEEE International Conference on Cluster Computing

作者： Xusheng Zhang Ziyu Shen Bin Xia Zheng Liu Yun Li Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications

ISBN: (数字)9781728166773

ISBN: (纸本)9781728166780

Virtualization technologies provide solutions of cloud computing. Virtual resource scheduling is a crucial task in data centers, and the power consumption of virtual resources is a critical foundation of virtualization scheduling. Containers are the smallest unit of virtual resource scheduling and migration. Although many effective models for estimating power consumption of virtual machines (VM) have been proposed, few power estimation models of containers have been put forth. In this paper, we offer a fast-training piecewise regression model based on decision tree to build a VM power estimation model and estimate the containers' power by treating the container as a group of processes on the VM. In our model, we characterize the nonlinear relationship between power and features and realize the effective estimation of the containers on the VM. We evaluate the proposed model on 13 workloads in PARSEC and compare it with several models. The experimental results prove the effectiveness of our proposed model on most workloads. Moreover, the estimated power of the containers is in line with expectations.

关键词： Power demand Processor scheduling Estimation Containers Virtual machining data models Virtualization

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A Lexical Updating Algorithm for Sentiment Analysis on Chinese Movie Reviews 5

A Lexical Updating Algorithm for Sentiment Analysis on Chine...

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5th International Conference on Advanced Cloud and big data, CBD 2017

作者： Song, Yiwei Gu, Kaiwen Li, Huakang Sun, Guozi Bell Honor School Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781538610725

With the prevalence of Internet, sentiment analysis gets popularity among the world. Researchers have made use of kinds of online documents like commodities reivews and movie reviews as training samples to train their models and classfiers, by which they could speculate the underlying emotion in new ones. Douban is a Chinese online community where users share their personal reviews to express their feelings about movies. Those Chinese movie reviews were utilized by us to train our lexicon-based model. Yet multiple words in a ready-made lexicon do not agree with the movie reviews in a specific domain, which means the original lexicon acquires being updated to gain higher accuracy. In this paper we introduce a lexical updating algorithm based on a widely used lexicon. After turns of training of updating, this lexicon is capable of classifying sentiment among movie reviews. The experimental result shows our model using the updated lexicon could get a better performance than the primitive lexicon-based model. © 2017 IEEE.

关键词： Sentiment analysis

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Identifying Outliers in Astronomical Images with Unsupervised Machine Learning

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Research in Astronomy and Astrophysics 2022年第8期22卷 74-84页

作者： Yang Han Zhiqiang Zou Nan Li Yanli Chen School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China 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

Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in *** unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey ***,it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant *** learning is also unsuitable for this purpose because designing proper training sets for unanticipated signals is *** by these challenges,we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical *** comparison,we construct three methods,which are built upon the k-nearest neighbors(KNN),Convolutional Auto-Encoder(CAE)+KNN,and CAE+KNN+Attention Mechanism(att CAE_KNN)*** sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above *** show that att CAE_KNN achieves the best recall(78%),which is 53%higher than the classical KNN method and 22%higher than CAE+*** efficiency of att CAE_KNN(10 minutes)is also superior to KNN(4 h)and equal to CAE+KNN(10 minutes)for accomplishing the same ***,we believe that it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised ***,we will apply att CAE_KNN to available survey data sets to assess its applicability and reliability.

关键词： Galaxy Physical data and Processes Galaxy:fundamental parameters

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A survey on trust modeling from a bayesian perspective

arXiv

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arXiv 2018年

作者： Liu, Bin School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Big Data Security & Intelligent Processing

In this paper, we are concerned with trust modeling for agents in networked computing systems. As trust is a subjective notion that is invisible, implicit and uncertain in nature, many attempts have been made to model trust with aid of Bayesian probability theory, while the field lacks a global comprehensive analysis for variants of Bayesian trust models. We present a study to fill in this gap by giving a comprehensive review of the literature. A generic Bayesian trust (GBT) modeling perspective is highlighted here. It is shown that all models under survey can cast into a GBT based computing paradigm as special cases. We discuss both capabilities and limitations of the GBT perspective and point out open questions to answer for advancing it to become a pragmatic infrastructure for analyzing intrinsic relationships between variants of trust models and for developing novel ones for trust evaluation. Copyright © 2018, The Authors. All rights reserved.

关键词： Surveys

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Visco-acoustic Wave-equation Traveltime Inversion with Correct and Incorrect Attenuation Profiles 87

Visco-acoustic Wave-equation Traveltime Inversion with Corre...

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Society of Exploration Geophysicists International Exposition and 87th Annual Meeting, SEG 2017

作者： Yu, Han Chen, Yuqing Guo, Bowen Schuster, Gerard T. Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210003 China

A visco-acoustic wave-equation traveltime inversion method is presented that inverts for a shallow subsurface velocity distribution with correct and incorrect attenuation profiles. Similar to the classical wave equation traveltime inversion, this method applies the misfit functional that minimizes the first break differences between the observed and predicted data. Although, WT can partly avoid the cycle skipping problem, an initial velocity model approaches to the right or wrong velocity models under different setups of the attenuation profiles. However, with a Q model far away from the real model, the inverted tomogram is obviously different from the true velocity model while a small change of the Q model does not improve the inversion quality in a strong manner if low frequency information is not lost.. © 2017 SEG.

关键词： Wave equations

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A privacy-preserving multi-keyword ranked search over encrypted data in hybrid clouds 5th

A privacy-preserving multi-keyword ranked search over encryp...

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

作者： Dai, Hua Ji, Yan Liu, Liang Yang, Geng Yi, Xun Nanjing University of Post and Telecommunication Nanjing210023 China Jiangsu Security and Intelligent Processing Lab of Big Data Nanjing210023 China Nanjing University of Aeronautics and Astronautics Nanjing210016 China Royal Melbourne Institute of Technology University Melbourne3001 Australia

ISBN: (纸本)9783030242701

Due to the convenience, economy and high scalability of cloud computing, more and more individuals and enterprises are motivated to outsource their data or computing to clouds. In this paper, we propose a privacy-preserving multi-keyword ranked search over encrypted data in hybrid cloud, which is denoted as MRSE-HC. The keyword partition vector model is presented. The keyword dictionary of documents is clustered into balanced partitions by a bisecting k-means clustering based keyword partition algorithm. In accordance with the partitions, the keyword partition based bit vectors are defined for documents and queries which are utilized as the index of searches. The private cloud filters out the candidate documents by the keyword partition based bit vectors, and then the public cloud uses the trapdoor to determine the result in the candidates. The security analysis and performance evaluation show that MRSE-HC is a privacy-preserving multi-keyword ranked search scheme for hybrid clouds and outperforms the existing scheme FMRS in terms of search efficiency. © Springer Nature Switzerland AG 2019.

关键词： Privacy-preserving techniques

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User Grouping for Sharing Services with Capacity Limit

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2021年第2期14卷 614-627页

作者： Liu, Xiping Dou, Wanchun Wang, Xin Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Security& Intelligent Pr Sch Comp Sci Nanjing 210023 Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Dept Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China SUNY Stony Brook Wireless Networking & Syst Lab Dept Elect & Comp Engn Stony Brook NY 11794 USA

Sharing a service among multiple users could bring benefit to users by reducing their service price and also benefit service providers by allowing them to make more profit. Shared services usually have a capacity limit. To construct a win-win situation between users and providers through service sharing, it is necessary to reasonably organize users with similar service requests into groups under the limits of group sizes. This paper explores methods to enable user grouping for sharing services with the limit on the group size. Based on user request descriptions and evaluation, a connection relation is built to present which two users could be in a group. Semi-groups can be derived from the connection relation and two grouping schemes satisfying the group size limit are further determined through proposed algorithms to meet different service expectations. Finally, a case study and a simulation are given to demonstrate the effectiveness of the proposed methods in grouping users to provide higher service benefits to both users and providers.

关键词： Quality of service Public transportation Companies Computer science Service computing Smart devices Service sharing grouping services with capacity limit asymmetric similarity

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GNEA: A Novel GCN-based Network Embedding Algorithm for Semantic Social Network 10

GNEA: A Novel GCN-based Network Embedding Algorithm for Sema...

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10th International Symposium on System security, Safety, and Reliability, ISSSR 2024

作者： Zhao, Jinbo He, Jinling Du, Yuanhan Tang, Ming Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

With the development of semantic social networks, social networks become more complex and their size expands rapidly, which brings significant challenges to social network analysis. Network Embedding can transform the network from a high-dimensional adjacency matrix to a low-dimensional one, which has excellent foreground in link prediction, vertex classification, and graph visualization. Nowadays, most algorithms are proposed for static networks with little consideration of the dynamic changes in the network. However, most real-world networks change over time. Directly applying static network embedding algorithms to dynamic networks will result in poor stability, flexibility, and efficiency. Because of these shortcomings, we propose a graph convolution-based dynamic network representation learning algorithm (GNEA), which combines Graph Convolutional Network (GCN) with Long Short-Term Memory (LSTM) to train the time series networks. GCN is applied to extract features in the network, and LSTM is utilized to capture temporal information. GNEA can not only perform characterization learning on the network but also obtain more abundant information during the dynamic evolution of the network. GNEA is compared with several typical algorithms on the datasets Stochastic Block Model (SBM), Autonomous Systems (AS), and Digital Bibliography and Library Project (DBLP). The results prove that GNEA has good performance in MAP and precision@k. © 2024 IEEE.

关键词： Complex networks

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Semi-supervised Deep Closest Point Method for Point Cloud Registration

Semi-supervised Deep Closest Point Method for Point Cloud Re...

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2021 International Conference on Robotics Automation and intelligent Control, ICRAIC 2021

作者： Wu, Yong Shen, Lingfeng Li, Ping Jiangsu Power Transmission and Transformation Co. Ltd. Nanjing211106 China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

Point cloud registration is one of the key issues in fields that need 3D scenes with global vision, including 3D scene reconstruction in robot technology, high-accuracy 3D map reconstruction in automatic driving, 3D reconstruction of real-time monitoring underground tunnel. Recently, point cloud registration methods based on deep neural networks have made great progress by learning from a large amount of well-aligned point cloud pairs. However, these large-scale labelled samples are usually difficult to obtain in the real-world applications such as visual simultaneous location and mapping of mobile robots which obtains unlabelled scene images in real time. To solve this problem, we propose a deep point cloud registration method based on the semi-supervised learning. Specifically, for the problem of less supervised data, we use Iterative Closest Point (ICP) algorithm to generate pseudo labels for the point cloud samples with unknown rigid transformations, effectively increasing the number of supervised data;in order to amend the pseudo labels, we design an alternating optimization algorithm to jointly learn the pseudo labels and deep point cloud registration model. The deep point cloud registration model and ICP are used alternately to continuously improve the precision of pseudo labels and the performance of the deep model. Experimental results show that this method can still obtain a reliable point cloud registration model even when just observing a small amount of point cloud samples with ground truth labels. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Surface measurement

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