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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Guangping Xu Sheng Lin Gang Wang Xiaoguang Liu Kai Shi Hua Zhang Tianjin Key Lab. of Intelligence Computing and New Software Technology Tianjin China College of Information Tech. and Science Nankai University Tianjin China School of Computer and Communication Tianjin University of Technology Tianjin China

Heterogeneity is the natural feature in distributed networks. Different from the traditional disk array, the amount of data allocated on heterogenous peers may be not the same. To maximize the reliability of stored data objects in heterogeneous networks, the optimal allocation of erasure-coded fragments is a challenging problem constrained with heterogeneous peer availab.lities and redundancy overhead. This paper examines this optimal problem considered MDS erasure codes applied into distributed storage networks. First, we model the reliability of an allocation with the weighted-k-out-of-s model and extend its properties to efficiently calculate the reliability of an allocation; then we reduce the reliability computation of a given allocation to linear computation cost based on the weighted k-out-of-s model. Then, we deduce the problem to integer partition problem and propose two order-based search algorithms. Our experiments show that our proposed algorithms can be applied to find the optimal allocations efficiently in various practical coding cases. Furthermore, we evaluate the performance of our proposed search algorithms with some practical storage settings, and then present experimental results including the reliability, redundancy overheads and allocation pattern for the optimal allocation driven by practical network traces.

关键词： Resource management Reliability theory Availab.lity Computational modeling Redundancy Computer network reliability

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Context-aware computing approach with uncertainty for active application

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Journal of Information and Computational Science 2008年第1期5卷 179-186页

作者： Zhang, Degan Zhang, Hua Wang, Fayu Xue, Yabing School of Computer Science and Technology Tianjin University of Technology Tianjin 300191 China Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin 300191 China Key Lab. of Industrial Controlling Technology Zhejiang University Hangzhou 310027 China

For active application of many cases, context-aware computing with uncertainty includes forming model, fusing of aware context, managing context information and so on. Approach research on computing of aware context with uncertainty for making dynamic decision is focused in this paper. We compare dynamic context-aware computing with improved Random Set Theory (RST) and extended D-S Evidence Theory (EDS). We give new modeling mode based on RST for aware context and our computing approach of modeled aware context, extend classic D-S Evidence Theory after considering context's feature. Then compare relative computing methods, enumerate experimental examples and give the evaluation. By comparisons, the more validity of new context-aware computing approach based on RST than EDS with uncertainty information has been tested successfully.

关键词： Set theory

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A kind of new decision fusion method based on sensor evidence for active application

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Journal of Information and Computational Science 2008年第1期5卷 171-178页

作者： Zhang, Degan Zhang, Hua Ning, Hongyun Li, Wenjie School of Computer Science and Technology Tianjin University of Technology Tianjin 300191 China Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin 300191 China Key Lab. of Industrial Controlling Technology Zhejiang University Hangzhou 310027 China

During decision making procedure of active application, the primary signals with noise, which got from each sensor, were sent to a decision making center, the fusion center, through the distributed or centralized computer network. The fusion center then formed a final decision based on the primary decisions received from low-level sensors. But multi-sensor heterogeneous information fusion is an extremely challenging problem because of the complex and highly irregular nature of environment. We have designed a kind of new decision fusion method based on sensor evidence for active application in this paper. A fusion model, approach and relative lemma for multi-sensor heterogeneous information has been presented. For decision fusion, we have utilized the approaching relative efficiency performance measure. This method has been tested successfully by our active application projects.

关键词： Decision making

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An Adaptive POI Recommendation Algorithm by Integrating User’s Temporal and Spatial Features in LBSNs

An Adaptive POI Recommendation Algorithm by Integrating User...

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作者： Miao Li Wenguang Zheng Yingyuan Xiao Xu Jiao Tianjin Key Laboratory of Intelligence Computing & New Software Technology Tianjin University of Technology Department of Liberal Education Tianjin Foreign Studies University

With the rapid development of Internet technology and global positioning technology, location-based social networks（LBSNs）have emerged. Massive check-in data generated by LBSNs can be employed to explore users’ behavior preferences, and enable a wide range of location-based social services. The most straightforward service is Point of Interest（POI） recommendation, which help users to discover their desired places. In order to achieve effective and precise POI recommendation, complicated high-dimensional data need to be handled, i.e., user-geographical position-visiting timecategory of the visiting place. Besides, such data usually suffer from a data sparsity problem. To this end, this paper proposes a novel POI recommendation model that incorporates spatial and temporal preferences of the users. We propose a tensor factorization-based approach and a Voronoi diagram-based approach to model the impact of temporal and spatial features on users’ preference, respectively. Then, a fusion framework is proposed to calculate similarities between different uses by combining both temporal and spatial preferences of the ***, an adaptive collab.rative filtering approach is applied to generate recommendation list. The experiments results on the Foursquare and the Gowalla datasets shows that our model has higher recommendation performance than other comparison models.

关键词： POI Recommendation Temporal Spatial Tensor Factorization

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Network measurement based redundancy model and maintenance in dynamic P2P storage systems

Network measurement based redundancy model and maintenance i...

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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Guangping Xu Hua Zhang Jing Liu Gang Wang Xiaoguang Liu The Key Laboratory of Intelligence Computing & New Software Technology Tianjin TianJin University of Technology Tianjin China Information Technology Science College Nankai University Tianjin China

Peer-to-peer distributed storage systems aggregate the storage space of many peers spread over the Internet. Due to the dynamic and scalab.e nature of these systems, it is a challenging issue to access data in an availab.e and reliable way through redundancy. Following the modeling methodology presented, we present the stochastic model to analyze redundancy evolution of these systems under churn. Different from the previous work based on the average peer availab.lity, the stochastic model can be applied into the practice based on both conditional probabilities (alpha, theta) which can be obtained from network probing easily. First, we apply the model to characterize the redundancy evolution of a fragment system with temporary churn. Then, we use an empirical trace and a synthetic trace to validate the model. Second, based on the characteristics of different churn from the both probabilities, we propose the redundancy maintenance strategy assisted by network sampling. Our simulations evaluate the performance of the strategy driven by empirical and synthetic traces.

关键词： Redundancy Availab.lity Space technology Peer to peer computing Stochastic systems Stochastic processes Sampling methods software measurement lab.ratories Intelligent networks

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Code Difference Guided Adversarial Example Generation for Deep Code Models

Code Difference Guided Adversarial Example Generation for De...

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IEEE International Conference on Automated software Engineering (ASE)

作者： Zhao Tian Junjie Chen Zhi Jin College of Intelligence and Computing Tianjin University Tianjin China Key Lab of High Confidence Software Technology Peking University Beijing China

Adversarial examples are important to test and enhance the robustness of deep code models. As source code is discrete and has to strictly stick to complex grammar and semantics constraints, the adversarial example generation techniques in other domains are hardly applicable. Moreover, the adversarial example generation techniques specific to deep code models still suffer from unsatisfactory effectiveness due to the enormous ingredient search space. In this work, we propose a novel adversarial example generation technique (i.e., CODA) for testing deep code models. Its key idea is to use code differences between the target input (i.e., a given code snippet as the model input) and reference inputs (i.e., the inputs that have small code differences but different prediction results with the target input) to guide the generation of adversarial examples. It considers both structure differences and identifier differences to preserve the original semantics. Hence, the ingredient search space can be largely reduced as the one constituted by the two kinds of code differences, and thus the testing process can be improved by designing and guiding corresponding equivalent structure transformations and identifier renaming transformations. Our experiments on 15 deep code models demonstrate the effective-ness and efficiency of CODA, the naturalness of its generated examples, and its capability of enhancing model robustness after adversarial fine-tuning. For example, CODA reveals 88.05 % and 72.51 % more faults in models than the state-of-the-art techniques (i.e., CARROT and ALERT) on average, respectively.

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Code Difference Guided Adversarial Example Generation for Deep Code Models 23

Code Difference Guided Adversarial Example Generation for De...

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Proceedings of the 38th IEEE/ACM International Conference on Automated software Engineering

作者： Zhao Tian Junjie Chen Zhi Jin College of Intelligence and Computing Tianjin University Tianjin China Key Lab of High Confidence Software Technology Peking University Beijing China

ISBN: (纸本)9798350329964

Adversarial examples are important to test and enhance the robustness of deep code models. As source code is discrete and has to strictly stick to complex grammar and semantics constraints, the adversarial example generation techniques in other domains are hardly applicable. Moreover, the adversarial example generation techniques specific to deep code models still suffer from unsatisfactory effectiveness due to the enormous ingredient search space. In this work, we propose a novel adversarial example generation technique (i.e., CODA) for testing deep code models. Its key idea is to use code differences between the target input (i.e., a given code snippet as the model input) and reference inputs (i.e., the inputs that have small code differences but different prediction results with the target input) to guide the generation of adversarial examples. It considers both structure differences and identifier differences to preserve the original semantics. Hence, the ingredient search space can be largely reduced as the one constituted by the two kinds of code differences, and thus the testing process can be improved by designing and guiding corresponding equivalent structure transformations and identifier renaming transformations. Our experiments on 15 deep code models demonstrate the effectiveness and efficiency of CODA, the naturalness of its generated examples, and its capability of enhancing model robustness after adversarial fine-tuning. For example, CODA reveals 88.05% and 72.51% more faults in models than the state-of-the-art techniques (i.e., CARROT and ALERT) on average, respectively.

关键词： adversarial example

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Code Difference Guided Adversarial Example Generation for Deep Code Models

arXiv

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arXiv 2023年

作者： Tian, Zhao Chen, Junjie Jin, Zhi College of Intelligence and Computing Tianjin University Tianjin China Key Lab of High Confidence Software Technology Peking University Beijing China

Adversarial examples are important to test and enhance the robustness of deep code models. As source code is discrete and has to strictly stick to complex grammar and semantics constraints, the adversarial example generation techniques in other domains are hardly applicable. Moreover, the adversarial example generation techniques specific to deep code models still suffer from unsatisfactory effectiveness due to the enormous ingredient search space. In this work, we propose a novel adversarial example generation technique (i.e., CODA) for testing deep code models. Its key idea is to use code differences between the target input (i.e., a given code snippet as the model input) and reference inputs (i.e., the inputs that have small code differences but different prediction results with the target input) to guide the generation of adversarial examples. It considers both structure differences and identifier differences to preserve the original semantics. Hence, the ingredient search space can be largely reduced as the one constituted by the two kinds of code differences, and thus the testing process can be improved by designing and guiding corresponding equivalent structure transformations and identifier renaming transformations. Our experiments on 15 deep code models demonstrate the effectiveness and efficiency of CODA, the naturalness of its generated examples, and its capability of enhancing model robustness after adversarial fine-tuning. For example, CODA reveals 88.05% and 72.51% more faults in models than the state-of-the-art techniques (i.e., CARROT and ALERT) on average, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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An Efficient Algorithm for Accurate Sensor Data Aggregation

An Efficient Algorithm for Accurate Sensor Data Aggregation

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2015 International Conference on Informatics, Control and Automation(ICA 2015)

作者： Ying-yuan XIAO Xin-rong ZHAO Yan SHEN Hua-feng DENG Ching-hsien HSU Tianjin University of Technology Tianjin Key Lab of Intelligence Computing and Novel Software Tech Jiangxi Normal University "Chung Hua University"

Wireless sensor networks have received considerable attention in many applications. The widely used multipath routing-based aggregation in wireless sensor networks typically suffers from the problem of duplicates. In this paper, we proposed FL algorithm to deal with the problem. The main idea of FL algorithm is to use sketch to represent the reading and transform the sensitive aggregation into insensitive aggregation. FL algorithm integrates FM-sketch and LC-sketch to compute the approximate aggregation results which satisfy the accuracy requirements. In order to verify the performance of the proposed algorithm, we implement extensive experiments through the simulation software. The results show that the proposed algorithm is feasible and effective.

关键词： Wireless sensor network Data aggregation Duplicate

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A FUZZY IDENTIFICATION METHOD OF CONTOURS BASED ON CHAIN-CODE FEATURES

A FUZZY IDENTIFICATION METHOD OF CONTOURS BASED ON CHAIN-COD...

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第三届全国智能视觉监控学术会议

作者： Qi Dong Guangping Xu Liu Jing Yanbing Xue Hua Zhang Key Laboratory of Intelligence Computing and New Software Technology TianjinChina300384 Key Laboratory of Computer Vision and SystemMinistry of EducationTianjinChina300384 School of Computer and CommunicationTianjin University of TechnologyTianjinChina300384

ISBN: (纸本)9781457718342

The contour analysis and identification are the important aspects in visual surveillance *** paper proposes a fuzzy identification method of ***,according to the description of a contour based on the chain-code method,the proposed method utilizes the statistical features of contours including the chain-code entropy and chain-code space distribution entropies,from which the feature vector of a contour is ***,the method generates the contour pattern from some contour samples and uses the approaching principle to identify a *** our method integrates effectively multiple statistical feathers of its chain-code and employs a fuzzy pattern recognition technique,the experiments show quantitatively that it can achieve good results from various metrics.

关键词： Image Contour Fuzzy Pattern Chain Code Object Recognition

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