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Proceedings of the 8th ACM international conference on Autonomic computing

作者： Shan, Zhiyong Wang, Xin Chiueh, Tzi-Cker Meng, Xiaofeng Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing China Stony Brook University Stony Brook NY United States Industrial Technology Research Institute Taiwan

ISBN: (纸本)9781450306072

A common application of virtual machines (VM) is to use and then throw away, basically treating a VM like a completely isolated and disposable entity. The disadvantage of this approach is that if there is no malicious activity, the user has to re-do all of the work in her actual workspace since there is no easy way to commit (i.e., merge) only the benign updates within the VM back to the host environment. In this work, we develop a VM commitment system called Secom to automatically eliminate malicious state changes when merging the contents of an OS-level VM to the host. Secom consists of three steps: grouping state changes into clusters, distinguishing between benign and malicious clusters, and committing benign clusters. Secom has three novel features. First, instead of relying on a huge volume of log data, it leverages OS-level information flow and malware behavior information to recognize malicious changes. As a result, the approach imposes a smaller performance overhead. Second, different from existing intrusion detection and recovery systems that detect compromised OS objects one by one, Secom classifies objects into clusters and then identifies malicious objects on a cluster by cluster basis. Third, to reduce the false positive rate when identifying malicious clusters, it simultaneously considers two malware behaviors that are of different types and the origin of the processes that exhibit these behaviors, rather than considers a single behavior alone as done by existing malware detection methods. We have successfully implemented Secom on the Feather-weight Virtual Machine (FVM) system, a Windows-based OS-level virtualization system. Experiments show that the prototype can effectively eliminate malicious state changes while committing a VM with small performance degradation. Moreover, compared with the commercial anti-malware tools, the Secom prototype has a smaller number of false negatives and thus can more thoroughly clean up malware side effects. In addit

关键词： Virtual machine

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Communication with children for older adults' life satisfaction: Non-face-to-face and/or face-to-face?

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Gerontechnology 2016年第4期14卷 191-203页

作者： Sun, Kai Zuo, Meiyun Kong, Dong Key Laboratory of Data Engineering and Knowledge Engineering School of Business Renmin University of China Beijing China Research Institute of Smart Aged Care School of Information National Academy of Development and Strategy Renmin University of China Beijing China

Objectives Communication with children is an important way to reduce the effect of empty nest syndrome and increase life satisfaction of older adults. Under new network environment, more and more young people rely on non-face-to-face (nFTF) technology to communicate with parents, instead of face-to-face (FTF) communication due to the constraints of living condition and busy schedule of job. We are wondering whether it is good for older adults' life satisfaction. Specifically, can nFTF provide the same benefit as FTF does to increase older adults' life satisfaction? How to use nFTF to benefit older adults best? Theories From the perspective of family support (for instance, emotional support and instrumental support), this paper mainly uses Media-Richness Theory and Social Presence Theory to analyse the different capability of FTF and nFTF to increase older adults' life satisfaction. Besides, we try to examine in which situation increasing nFTF can benefit older adults. Design The data of China Health and Retirement Longitudinal Study (CHARLS) is used to test our hypotheses. Results FTF is better than nFTF to increase life satisfaction. Only in the context that FTF is harder or less available (frequency of FTF is less than once every six months), nFTF is positively related to increasing life satisfaction;when the frequency of FTF is more than once every six months, more nFTF will not provide an extra benefit for older adults. In the present paper, we systematically analyse the difference of FTF and nFTF, and extend previous studies, which focus on nFTF's advantage, to in what context nFTF can cooperate with FTF to increase older adults' life satisfaction.

关键词： Face-to-face Life satisfaction Non-face-to-face Older adults

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Study on Family Relations Reasoning Based on Automated Reasoning

Study on Family Relations Reasoning Based on Automated Reaso...

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International Conference on Computational Intelligence and Software engineering (CiSE)

作者： Qiang Ge Fengbin Zheng Institute of Data and Knowledge Engineering Henan University KaiFeng China

Automated reasoning has become the research hotspot among researchers, and family relations are common logic knowledge, which can be reasoned. This paper presents four basic relations on family relations, which can deduce others new complex relations. The relation knowledge was stored in knowledge database as forms of basic relations. Some common questions could be deduced successfully and receive correct answers. The study gives an intelligent inference procedure on family relations and two algorithms, one algorithm is Positive Retrieving Information Algorithm (PRIA), and the other is Negative Recombination Information Algorithm (NRIA), which could be well programmed.

关键词： Cognition Inference algorithms Computers knowledge engineering Geometry Presses Natural languages

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Personalized Recommendation Based on Entity Attributes and Graph Features 12

Personalized Recommendation Based on Entity Attributes and G...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Zhu, Yi Dong, Bingbing Sha, Zhiqing Yangzhou University School of Information Engineering Yangzhou China Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institute of Big Knowledge Science Hefei University of Technology Hefei China

ISBN: (纸本)9781665438582

With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the information which users really need on the website. Many recent studies have introduced additional attribute information about users and/or items to the rating matrix for alleviating the problem of data sparsity. In order to make full use of the attribute information and scoring matrix, deep learning based recommendation methods are proposed, especially the autoencoder model has attracted much attention because of its strong ability to learn hidden features. However, most of the existing autoencoder- based models require that the dimension of the input layer is equal to the dimension of the output layer, which may increase model complexity and certain information loss when using attribute information. In addition, as users' awareness of privacy protection increases, user attribute information is difficult to obtain. To address the above problems, in this paper, we propose a hybrid personalized recommendation model, which uses a semi-autoencoder to jointly embed the item's score vector and internal graph features (short for Co-Agpre). Specifically, we regard the user-item historical interaction matrix as a bipartite graph, and the Laplacian of the user-item co-occurrence graph is utilized to obtain the graph features of the item for solving the problem of sparse attributes. Then a semi-autoencoder is introduced to learn the hidden features of the item and perform rating prediction. The proposed model can flexibly use information from different sources to reduce the complexity of the model. Experiments on two real-world datasets demonstrate the effectiveness of the proposed Co-Agpre compared with state-of-the-art methods. © 2021 IEEE.

关键词： Websites

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Ubiquitous Social Media Analysis 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Martin Atzmueller Alvin Chin Denis Helic Andreas Hotho

ISBN: (数字)9783642453922

ISBN: (纸本)9783642453915

This book constitutes the thoroughly refereed joint post-proceedings of the Third International Workshop on Mining Ubiquitous and Social Environments, MUSE 2012, held in Bristol, UK, in September 2012, and the Third International Workshop on Modeling Social Media, MSM 2012, held in Milwaukee, WI, USA, in June 2012. The 8 full papers included in the book are revised and significantly extended versions of papers submitted to the workshops. They cover a wide range of topics organized in three main themes: communities and group structure in ubiquitous social media; ubiquitous modeling and aspects of social interactions and influence.

关键词： Information Storage and Retrieval data Mining and knowledge Discovery Discrete Mathematics in Computer Science Artificial Intelligence

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A quadtree based dynamic attribute index structure and query process

A quadtree based dynamic attribute index structure and query...

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International Conference on Computer Networks and Mobile Computing (ICCNMC)

作者： Rui Ding Xiaofeng Meng Institute of Data and Knowledge Engineering Renmin University of China Beijing China

With rapid development of wireless communications as well as positioning technologies, the concept of moving objects has become more and more important, which are challenging the traditional database technologies. Since moving objects change their dynamic attributes continuously, it is impractical to issue explicit updates for every change. In this paper, we introduce an indexing structure based on quadtree, which can be applied to one-dimensional, two-dimensional, three-dimensional and any other multi-dimensional abstract data space. We also present the querying process based on the indexing structure.

关键词： Query processing databases Space technology Indexing Mobile handsets Wireless communication knowledge engineering Technology management Internet Information management

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Mining closed frequent itemset based on FP-Tree

Mining closed frequent itemset based on FP-Tree

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IEEE International Conference on Granular Computing (GRC)

作者： Shengwei Li Lingsheng Li Chong Han Institute of Data and Knowledge Engineering Henan University Kaifeng Henan China

Closed frequent itemsets provide a minimal representation of the itemset without losing their support information and they can greatly reduce the number of patterns. So how to obtain all the closed frequent itemsets effectively is of good importance. This paper constructs a FP-tree according the matrix which only scans the database once. Also we design an algorithm to mine closed frequent itemsets which use the structure of the FP-tree. We construct a link for every single node in the FP-tree, and then give the closed frequent itemsets and the pruning of the link. This algorithm is an algorithm which only scans the database once, also it avoids the defect about patterns tree based on terms and can mine the closed frequent items efficiently.

关键词： data mining Itemsets Transaction databases knowledge engineering data structures Tree data structures Algorithm design and analysis Association rules Pattern analysis

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A policy conflict detecting algorithm of RBAC based on concept lattice model

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International Journal of Hybrid Information Technology 2015年第7期8卷 27-36页

作者： Han, Daojun Zhang, Lei Shen, Xiajiong Jia, Peiyan Institute of Data and Knowledge Engineering Henan University KaiFeng475004 China

Access control policy conflict detecting is an important issue in the usage of information system. To solve the problem that the expression of existing common Role-Based Access Control(RBAC) policy conflict detecting is not intuitive and the corresponding algorithm is not efficient, meanwhile, we observed that the concept lattice model has natural advantages being a data representation method and is easy to be combined with RBAC. Thus, this paper introduce the concept lattice model into RBAC policy conflict detecting algorithm, aim at the problems of jurisdiction conflict, static role conflict, and user conflict, utilizing the sub lattice on the basis of use two formal contexts to denote user-role relation and role-permission relation respectively, we provide an intuitive and efficient detecting algorithm. Experimental result shows the efficient of our algorithm. © 2015 SERSC.

关键词： Access control

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A finite ranked poset and its application in visualization of association rules

A finite ranked poset and its application in visualization o...

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IEEE International Conference on Granular Computing (GRC)

作者： Baoqing Jiang Chong Han Xiaohua Hu Institute of Data and Knowledge Engineering Henan University Kaifeng Henan China

ISBN: (纸本)9781424425129

We discuss the set of all Boolean association rules. By defining special partial order on the set, we get an isomorphism between the set and a special finite ranked poset. Through discussing some basic properties of the finite ranked poset, we can clearly represent the hierarchical structure of all Boolean association rules. Meanwhile, the Hasse diagram of the poset offers a visualization view of the structure.

关键词： Association rules Finite element methods data mining data visualization databases Adaptation model Information processing

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Development of Prediction System for Ship Movements Using Machine Learning and Radar Images

Development of Prediction System for Ship Movements Using Ma...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Nishizaki, Chihiro Takenaka, Masako Hirai, Yurie Okazaki, Tadatsugi Kobayashi, Mitsuru Tokyo University of Marine Science and Technology Dept. of Maritime System Engineering Tokyo Japan National Maritime Research Institute Dept. of Knowledge and Data System Tokyo Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

To maintain ship navigation safety, the navigator must understand and predict the movements of other ships. Radar images include both ship (target) and non-ship images (noise), such as sea clutter. To understand the movements of other ships, navigators must detect ship images from radar images manually. To maintain the optimum situation awareness of navigators while reducing the workload, a function that can automatically detect and track ship images, including small ships, is desired on the radar. Therefore, a system to predict ship movements using machine learning and radar images is proposed in this study. The proposed prediction system is based on a learning model developed using a denoising convolutional autoencoder. The learning and validation data are radar images processed via image processing in advance. The prediction accuracy of ship movements in this study is 90.97%, and the loss is 0.0396. © 2022 IEEE.

关键词： Convolution

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