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Two-phase locality-sensitive hashing for privacy-preserving distributed service recommendation 1

9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Qi, Lianyong Dou, Wanchun Zhang, Xuyun School of Information Science and Engineering Qufu Normal University Jining China Chinese Academy of Education Big Data Qufu Normal University Jining China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand

ISBN: (数字)9783319694719

ISBN: (纸本)9783319694702

With the ever-increasing volume of services registered in various web communities, it becomes a challenging task to find the web services that a target user is really interested in from the massive candidates. In this situation, Collaborative Filtering (i.e., CF) technique is introduced to alleviate the heavy burden on the service selection decisions of target users. However, present CF-based recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios where data are multi-sourced. Furthermore, distributed service recommendation calls for the collaborations among multiple involved parties, during which the private information of users may be exposed. In view of these challenges, we propose a novel privacy-preserving distributed service recommendation approach based on two-phase Locality-Sensitive Hashing (LSH), named SerRectwo-LSH, in this paper. Concretely, in SerRectwo-LSH, we first look for the "similar friends" of a target user through a privacy-preserving two-phase LSH process;afterwards, we determine the services preferred by the "similar friends" of the target user, and then recommend them to the target user. Finally, through a set of experiments conducted on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation. © 2017, Springer International Publishing AG.

关键词： Efficiency

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Super-activating quantum memory with entanglement

arXiv

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arXiv 2017年

作者： Guan, Ji Feng, Yuan Ying, Mingsheng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China Center for Quantum Software and Information University of Technology SydneyNSW2007 Australia Department of Computer Science and Technology Tsinghua University Beijing100084 China

Noiseless subsystems were proved to be an efficient and faithful approach to preserve fragile information against decoherence in quantum information processing and quantum computation. They were employed to design a general (hybrid) quantum memory cell model that can store both quantum and classical information. In this paper, we find an interesting new phenomenon that the purely classical memory cell can be super-activated to preserve quantum states, whereas the null memory cell can only be super-activated to encode classical information. Furthermore, necessary and sufficient conditions for this phenomenon are discovered so that the super-activation can be easily checked by examining certain eigenvalues of the quantum memory cell without computing the noiseless subsystems explicitly. In particular, it is found that entangled and separable stationary states are responsible for the super-activation of storing quantum and classical information, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Chemical activation

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Mini-XML: An efficient mapping approach between XML and relational database

Mini-XML: An efficient mapping approach between XML and rela...

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International Conference on computer and Information science (ACIS)

作者： Huchao Zhu Huiqun Yu Guisheng Fan Huaiying Sun Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science and Engineering East China University of Science and Technology Shanghai China

ISBN: (纸本)9781509055081

In recent years, XML technology has won wide attention from both industry and academic. It can be used to mark data, define the data type and their own markup language. It is a cross-platform, context-dependent technology in the Internet environment and an effective tool for todays distributed structure information. The S-XML is a new approach for storing semi-structured data, and it supports query of the node in XML with SQL statements, which has shown impressive performance on many classic data sets. However, it is difficult to store XML data into a relational database, and the S-XML spends much more time and space to store the data. In this paper, we propose an efficient mapping approach, the mini-XML, to mapping XML into the relational database. In addition, path technique and position information are used to indicate the complex node relationship. Finally, two experiments are conducted to prove that the proposed method can achieve better performance in the decreasing of the storage time and storage space, especially dealing with the large amount of data.

关键词： XML Mini-XML Semi-structured Path technique extensible markup language Relational databases data types symbolic languages storage spaces MAPPING(MATHEMATICAL) mapping

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On-line memorry defragmentation for NVM-based persistent heaps 6

On-line memorry defragmentation for NVM-based persistent hea...

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6th IEEE Non-Volatile Memory Systems and Applications Symposium, NVMSA 2017

作者： Li, Lin Jiang, Wei Pan, Rui Zhan, Jinyu Shao, Zili Xiong, Xiankui School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Zhongxing Telecommunication Equipment Corporation Nanjing China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781538617687

Due to the durability of NVM, we are facing the great challenge of performing efficient memory defragmentation on persistent heaps. To address the problem, we propose an Online Persistent Memory Defragmentation (OPMD) method to manage persistent heaps. Employing two segment trees to track memory allocation and deallocation operations, our method can amortize the cost of global scan, and determine the most valuable objects needed to be moved in logarithmic time. OPMD adopts software transactional memory to move objects and updates object references in constant time using object metadata. We conduct extensive experiments to test the impact of OPMD on the throughput of the operations of linked list and hash table. © 2017 IEEE.

关键词： Resource management Metadata Nonvolatile memory Memory management Tracking Throughput Random access memory

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Credit evaluation of gas consumers by combining hierarchy analysis with clustering

Credit evaluation of gas consumers by combining hierarchy an...

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International Conference on computer and Information science (ACIS)

作者： Wenqing Xu Huiqun Yu Jianmei Guo Jinglei Shen Huaiying Sun Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science and Engineering East China University of Science and Technology Shanghai China

ISBN: (纸本)9781509055081

Credit evaluation of customer is an important task for gas companies to achieve marketing management, and is of great significance to improve the economic efficiency of enterprises. By analyzing various factors influencing gas customer credit, a gas customer credit index hierarchy is established. Then, based on the credit index hierarchy, this paper proposes a hybrid credit evaluation method by cluster analysis and analytic hierarchy process(AHP). This method first divides gas customers into different groups by cluster analysis, then determines the credit index weight by AHP, and finally evaluates gas customer credit rating by combining the above two results. The empirical analysis of the actual data of a gas company shows that as a synthesis of customer data's statistical properties and gas professionals' actual work experience, this model can evaluate gas customer credit rating rationally and effectively.

关键词： Indexes Analytic hierarchy process Clustering algorithms Algorithm design and analysis Safety Companies Economics

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Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms

arXiv

引用

arXiv 2017年

作者： Qian, Chao Yu, Yang Tang, Ke Yao, Xin Zhou, Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Shenzhen Key Laboratory of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization algorithm, and have shown empirically good performance in solving various real-word optimization problems. During the past two decades, promising results on the running time analysis (one essential theoretical aspect) of EAs have been obtained, while most of them focused on isolated combinatorial optimization problems, which do not reflect the general-purpose nature of EAs. To provide a general theoretical explanation of the behavior of EAs, it is desirable to study their performance on general classes of combinatorial optimization problems. To the best of our knowledge, the only result towards this direction is the provably good approximation guarantees of EAs for the problem class of maximizing monotone submodular functions with matroid constraints. The aim of this work is to contribute to this line of research. Considering that many combinatorial optimization problems involve non-monotone or non-submodular objective functions, we study the general problem classes, maximizing submodular functions with/without a size constraint and maximizing monotone approximately submodular functions with a size constraint. We prove that a simple multi-objective EA called GSEMO-C can generally achieve good approximation guarantees in polynomial expected running time. Copyright © 2017, The Authors. All rights reserved.

关键词： Computational complexity

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Connecting software metrics across versions to predict defects

arXiv

引用

arXiv 2017年

作者： Liu, Yibin Li, Yanhui Guo, Jianbo Zhou, Yuming Xu, Baowen State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has a significantly better effort-aware ranking effectiveness than the commonly used baseline models. Copyright © 2017, The Authors. All rights reserved.

关键词： Defects

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Multi-label learning with global and local label correlation

arXiv

引用

arXiv 2017年

作者： Zhu, Yue Kwok, James T. Zhou, Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances;or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult. That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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A new secure index supporting efficient index updating and similarity search on clouds 16

A new secure index supporting efficient index updating and s...

引用

4th ACM International Workshop on Security in Cloud Computing, SCC 2016

作者： Zhang, Baojia Zhang, He Yan, Boqun Zhang, Yuan State Key Laboratory for Novel Software Technology NanJing University Computer Science and Technology Department NanJing210046 China

ISBN: (纸本)9781450342858

With the increasing popularity of cloud storage services, many individuals and enterprises start to move their local data to the clouds. To ensure their privacy and data security, some cloud service users may want to encrypt their data before outsourcing them. However, this impedes efficient data utilities based on the plain text search. In this paper, we study how to construct a secure index that supports both efficient index updating and similarity search. Using the secure index, users are able to efficiently perform similarity searches tolerating input mistakes and update the index when new data are available. We formally prove the security of our proposal and also perform experiments on real world data to show its efficiency. © 2016 ACM.

关键词： Cloud storage

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VR-SGD: A simple stochastic variance reduction method for machine learning

arXiv

引用

arXiv 2018年

作者： Shang, Fanhua Zhou, Kaiwen Liu, Hongying Cheng, James Tsang, Ivor W. Zhang, Lijun Tao, Dacheng Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Centre for Artificial Intelligence University of Technology Sydney UltimoNSW2007 Australia National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the two vectors of VR-SGD are set to the average and last iterate of the previous epoch, respectively. The settings allow us to use much larger learning rates, and also make our convergence analysis more challenging. We also design two different update rules for smooth and non-smooth objective functions, respectively, which means that VR-SGD can tackle non-smooth and/or non-strongly convex problems directly without any reduction techniques. Moreover, we analyze the convergence properties of VR-SGD for strongly convex problems, which show that VR-SGD attains linear convergence. Different from most algorithms that have no convergence guarantees for non-strongly convex problems, we also provide the convergence guarantees of VR-SGD for this case, and empirically verify that VR-SGD with varying learning rates achieves similar performance to its momentum accelerated variant that has the optimal convergence rate O(1/T2). Finally, we apply VR-SGD to solve various machine learning problems, such as convex and non-convex empirical risk minimization, and leading eigenvalue computation. Experimental results show that VR-SGD converges significantly faster than SVRG and Prox-SVRG, and usually outperforms state-of-the-art accelerated methods, e.g., Katyusha. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimization

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