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41st International Conference on parallel Processing, ICPP 2012

作者： Yuan, Liang Ding, Chen Štefankovič, Daniel Zhang, Yunquan Lab. of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China Computer Science Department University of Rochester United States

ISBN: (纸本)9780769547961

An increasing number of applications in physical and social sciences require the analysis of large graphs. The efficiency of these programs strongly depends on their memory usage especially the locality of graph data access. Intuitively, the locality in computation should reflect the locality in graph topology. Existing locality models, however, operate either at program level for regular loops and arrays or at trace level for arbitrary access streams. They are not sufficient to characterize the relation between locality and connectivity. This paper presents a new metrics called the vertex distance and uses it to model the locality in breadth-first graph traversal (BFS). It shows three models that use the average node degree and the edge distribution to predict the number of BFS levels and the reuse distance distribution of BFS. Finally, it evaluates the new models using random and non-random graphs. © 2012 IEEE.

关键词： Graph theory

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of Computer science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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Study on parallel Computing

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Journal of Computer science & Technology 2006年第5期21卷 665-673页

作者：陈国良孙广中张云泉莫则尧 Anhui Province-MOST Key Co-Lab of High Performance Computing and Its Applications Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 P.R. China Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing 100080 P.R. China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 P.R. China

In this paper, we present a general survey on parallel computing. The main contents include parallel computer system which is the hardware platform of parallel computing, parallel algorithm which is the theoretical base of parallel computing, parallel programming which is the software support of parallel computing. After that, we also introduce some parallel applications and enabling technologies. We argue that parallel computing research should form an integrated methodology of ＂architecture algorithm programming application＂. Only in this way, parallel computing research becomes continuous development and more realistic.

关键词： parallel computing parallel architecture parallel programming parallel algorithm parallel application

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A capability-based matchmaking mechanism supporting resource aggregation within large-scale distributed computing infrastructures 4th

A capability-based matchmaking mechanism supporting resource...

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4th International Conference on Cloud Computing, CloudComp 2013

作者： Liang, Feng Liu, Hai Liu, Yunzhen Ma, Shilong Zheng, Siyao Deng, Pan State Key Lab of Software Development Environment Beihang University Beijing100191 China The Laboratory of Embedded Systems Beihang University Beijing100191 China Lab of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319055053

Facing the large-scale, heterogeneous dynamic resource and the complex constraints of computation-intensive parallel scientific applications, collaborating large-scale computation resource for these applications within Large-Scale Distributed Computing Infrastructures like grids and clouds are challenging. This paper addresses this issue by proposing a Resource Capability Model and implementing a corresponding language GSO and an information service Application Information Service to ensure not only the resource description, aggregation, matchmaking but also life cycle management of the reservations and tasks. Experiment result shows that our mechanism is scalable in both Grid and Cloud environment. © Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2014.

关键词： Information services

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Online Bayesian max-margin subspace multi-view learning 25

Online Bayesian max-margin subspace multi-view learning

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： He, Jia Du, Changying Zhuang, Fuzhen Yin, Xin He, Qing Long, Guoping Institute of Computing Technology CAS Beijing100190 China Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Last decades have witnessed a number of studies devoted to multi-view learning algorithms, however, few efforts have been made to handle online multi-view learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm to learn predictive subspace with max-margin principle. Specifically, we first define the latent margin loss for classification in the subspace, and then cast the learning problem into a variational Bayesian framework by exploiting the pseudo-likelihood and data augmentation idea. With the variational approximate posterior inferred from the past samples, we can naturally combine historical knowledge with new arrival data, in a Bayesian Passive-Aggressive style. Experiments on various classification tasks show that our model have superior performance.

关键词： Learning algorithms

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Learning beyond predefined label space via bayesian nonparametric topic modelling 1

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15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016

作者： Du, Changying Zhuang, Fuzhen He, Jia He, Qing Long, Guoping Institute of Computing Technology CAS Beijing100190 China Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9783319461281

ISBN: (纸本)9783319461274

In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data. To simultaneously recognize new data categories and assign most appropriate category labels to the data actually from known categories, existing models assume the number of unknown new categories is pre-specified, though it is difficult to determine in advance. In this paper, we propose a Bayesian nonparametric topic model to automatically infer this number, based on the hierarchical Dirichlet process and the notion of latent Dirichlet allocation. Exact inference in our model is intractable, so we provide an efficient collapsed Gibbs sampling algorithm for approximate posterior inference. Extensive experiments on various text data sets show that: (a) compared with parametric approaches that use pre-specified true number of new categories, the proposed nonparametric approach can yield comparable performance;and (b) when the exact number of new categories is unavailable, i.e. the parametric approaches only have a rough idea about the new categories, our approach has evident performance advantages. © Springer International Publishing AG 2016.

关键词： Inference engines

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A BPEL-based web service flow engine in the early warning of the volcano effusion 4th

A BPEL-based web service flow engine in the early warning of...

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4th International Conference on Cloud Computing, CloudComp 2013

作者： Pang, Jingyuan Wang, Chen Deng, Pan Lu, Yanhong Liu, Hao Earthquake Administration of Jilin Province Changchun30117 China Institute of Geophysics China Earthquake Administration Beijing China Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319055053

Web service flow engine is the core of the service assembling and cooperating system in volcano early warning data-shared platform. We design and realize a lightweight web service flow based on BPEL standard. It takes charge of parsing the service flow model constructed by users, checking-up the syntax and logic of the service flow file, constructing the basic elements of the given service flow, and then executing it according to the BPEL standard. Then the engine collects all the executing information and invoking results, at the same time it also supervises the execution of web services, managing the invocation of services. Finally it returns the results back to users. Experimental results show that our engine can work correctly, the time efficiency is high and the occupancy rate of system resources is nearly zero. © Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2014.

关键词： Web services

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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

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Computer Modeling in Engineering & sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China School of State Key Laboratory of Novel Software Technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision ***,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this ***,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision *** the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping ***,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision *** decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into ***,the decision variable with the strongest interaction is added to each *** minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different *** was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our *** with the other algorithms,our method is still at an advantage.

关键词： Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

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Multi-mode network analysis under differential privacy

Multi-mode network analysis under differential privacy

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2021 Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum, AIBDF 2021

作者： Song, Yuning Ding, Liping Dong, Mengying Liu, Xuehua Wang, Xiao Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing100190 China Guangzhou511458 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

With the advent of the big data era and the advancement of social network analysis, the public is increasingly concerned about the privacy protection in today's complex social networks. For the past few years, the rapid development of differential privacy (DP) technology, as a method with a reliable theoretical basis, can effectively solve the key problem of how to "disassociate" personal information in social networks. This paper focuses on the multi-mode heterogeneous network model which has attracted a lot of attention in the field of network research. It introduces differential privacy and its application in big social networks briefly first, and then proposes a centrality-analysis method based on DP in a typical social network, i.e. the multi-mode network. The calculation principle and applicable scenarios are discussed. Then, its utility is analyzed and evaluated through experimental simulation. Possible improvement of DP algorithm in multi-mode networks above is prospected in the end. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Heterogeneous networks

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EFFICIENT SPECTRAL METHODS FOR EIGENVALUE PROBLEMS OF THE INTEGRAL FRACTIONAL LAPLACIAN ONABALLOFANYDIMENSION

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Journal of computational Mathematics 2024年第4期42卷 1032-1062页

作者： Suna Ma Huiyuan Li Zhimin Zhang Hu Chen Lizhen Chen College of Science Nanjing University of Posts and TelecommunicationsNanjingChina State Key Laboratory of Computer Science/Laboratory of Parallel Computing Institute of SoftwareChinese Academy of SciencesBeijingChina Department of Mathematics Wayne State UniversityDetroitUSA School of Mathematical Sciences Ocean University of ChinaQingdaoChina Beijing Computational Science Research Center BeijingChina

An efficient spectral-Galerkin method for eigenvalue problems of the integral fractional Laplacian on a unit ball of any dimension is proposed in this *** symmetric positive definite linear system is retained explicitly which plays an important role in the numerical *** a sharp estimate on the algebraic system's condition number is established which behaves as N4s with respect to the polynomial degree N,where 2s is the fractional derivative *** regularity estimate of solutions to source problems of the fractional Laplacian in arbitrary dimensions is firstly investigated in weighted Sobolev *** the regularity of eigenfunctions of the fractional Laplacian eigenvalue problem is readily ***,rigorous error estimates of the eigenvalues and eigenvectors are *** experiments are presented to demonstrate the accuracy and efficiency and to validate the theoretical results.

关键词： Integral fractional Laplacian Spectral method Eigenvalue problem Regularity analysis Error estimate

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