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1.th International Conference on Information Security Practice and Experience, ISPEC 201.

作者： Zhou, Hongwei Wu, Xin Shi, Wenchang Yuan, Jinhui Liang, Bin Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Information Engineering University Zhengzhou China

ISBN: (纸本)9783319063195

Combining short instruction sequences originated only from existing code pieces, Return Oriented Programming (ROP) attacks can bypass the code-integrity effort model. To defeat this kind of attacks, current approaches check every instruction executed on a processor, which results in heavy performance overheads. In this paper, we propose an innovative approach, called HDROP, to detecting the attacks. It utilizes the observation that ROP attacks often make branch predictor in modern processors fail to determine the accurate branch destination. With the support of PMC (Performance Monitoring Counters) that is capable of counting performance events, we catch the abnormal increase in branch mis-prediction and detect the existence of ROP attacks. In HDROP, each basic unit being checked consists of hundreds of instructions rather than a single one, which effectively avoids significant performance overheads. The prototype system we developed on commodity hardware shows that HDROP succeeds in detecting ROP attacks, and the performance tests demonstrate that our approach has acceptably lower overheads. © 201. Springer International Publishing.

关键词： Computers

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Behavioural equivalences of a probabilistic pi-calculus

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Science China(Information Sciences) 2012年第9期55卷 2031-2043页

作者： CHEN WeiEn,CAO YongZhi & WANG HanPin key {1. of High Confidence Software Technologies,ministry of education of China,Institute of Software,School of Electronics engineering and Computer Science,Peking University,Beijing 1.0871.China 1. Key Laboratory of High Confidence Software Technologies Ministry of Education of China Institute of Software School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

Although different kinds of probabilistic π-calculus have been introduced and found their place in quantitative verification and evaluation,their behavioural equivalences still lack a deep *** propose a simple probabilistic extension of the π-calculus,π p,which is inspired by Herescu and Palamidessi’s probabilistic asynchronous π-*** early semantics of our π p is *** generalise several classic behavioural equivalences to probabilistic versions,obtaining the probabilistic(strong) barbed equivalence and probabilistic bisimulation for π *** we prove that the coincidence between the barbed equivalence and bisimilarity in the π-calculus is preserved in the probabilistic setting.

关键词： probabilistic pi-calculus behavioural equivalence probabilistic bisimulation probabilistic barbed bisimulation

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Recovering accurate labeling information from partially valid data for effective multi-label learning 29

Recovering accurate labeling information from partially vali...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Li, Ximing Wang, Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology China School of Computer Sci and Information Engineering Hefei University of Technology China

ISBN: (纸本)9780999241165

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial MultiLabel Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Learning systems

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Efficient SPARQL query processing in mapreduce through data partitioning and indexing

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1.th Asia Pacific Web Technology Conference, APWeb 201.

作者： Nie, Zhi Du, Fang Chen, Yueguo Du, Xiaoyong Xu, Linhao Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China IBM Research China Beijing China

ISBN: (纸本)9783642292521

Processing SPARQL queries on single node is obviously not scalable, considering the rapid growth of RDF knowledge bases. This calls for scalable solutions of SPARQL query processing over Web-scale RDF {1.. There have been attempts for applying SPARQL query processing techniques in MapReduce environments. However, no study has been conducted on finding optimal partitioning and indexing schemes for distributing RDF {1. in MapReduce. In this paper, we investigate RDF {1. partitioning technique that provides effective indexing schemes to support efficient SPARQL query processing in MapReduce. Our extensive experiments over a huge real-life RDF {1.set show the performance of the proposed partitioning and indexing schemes for efficient SPARQL query processing. © 201. Springer-Verlag Berlin Heidelberg.

关键词： MapReduce

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A hardware sandbox using processor virtualization for untrusted native code

A hardware sandbox using processor virtualization for untrus...

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201. International Forum on Computer and Information Technology, IFCIT 201.

作者： Wu, Xin Shi, Wen Chang Liang, Bin Key Laboratory of Data Engineering and Knowledge Engineering Renmin University Ministry Of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9783038350194

Native code which is in the form of browser plugins and native binaries in browser extensions brings significant security threats and has attracted considerable attention in recent years. Many researches focus on dangerous behaviors in browser extensions for new security countermeasures but there is no fundamental improvement. Constructing an individual isolation domain and providing sandboxed execution of the code is a key idea to address this problem. However current approach relies heavily on the trust of a module executing within the context of the whole untrusted browser code base accompanied with inevitable vulnerabilities. In this paper, we propose an innovative sandbox utilizing the hardware virtualization support available in modern Intel and AMD processors to construct an individual isolation domain for each benign-but-buggy native code. Our approach push the responsibilities for security down to the hardware level and minimize the difficulties encountered with software based approaches. By utilizing the processor instruction VMENTER or VMEXIT to cause the context switch, we can obtain a better system performance. © (201.) Trans Tech Publications, Switzerland.

关键词： Virtual reality

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Using New Fusion Operations to Improve Trust Expressiveness of Subjective Logic

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Wuhan University Journal of Natural Sciences 2011年第5期16卷 376-382页

作者： ZHOU Hongwei1.2,3,SHI Wenchang1.2,LIANG Zhaohui1.2,LIANG Bin1.2 1.key {1. of data engineering and knowledge engineering,ministry of education,Beijing 1.0872,China 2.School of Information,Renmin University of China,Beijing 1.0872,China 3.Institute of Electronic Technology,Information engineering University of PLA,Zhengzhou 450004,Henan,China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Institute of Electronic Technology Information Engineering University of PLA Zhengzhou Henan China

Subjective logic provides a means to describe the trust relationship of the realworld. However, existing fusion operations it offers Weal fused opiniotts equally, which makes it impossible to deal with the weighted opinions effectively. A. Jcsang presents a solution, which combines the discounting operator and the fusion operator to produce the consensus to the problem. In this paper, we prove that this approach is unsuitable to deal with the weighted opinions because it increases the uncertainty of the consensus. To address the problem, we propose two novel fusion operators that are capable of fusing opinions according to the weight of opinion in a fair way, and one of the strengths of them is improving the trust expressiveness of subjective logic. Furthermore, we present the justification on their definitions with the mapping between the evidence space and the opinion space. Comparisons between existing operators and the ones we proposed show the effectiveness of our new fusion operations.

关键词： trust subjective logic opinion fusion weizht

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The rewriting algorithm for disjunctive queries between peer ontologies

The rewriting algorithm for disjunctive queries between peer...

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3rd International Conference on Semantics, knowledge, and Grid, SKG 2007

作者： Qin, Biao Wang, Shan Dept. of Computer Science Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China

ISBN: (纸本)0769530079

We study the problem of answering queries given a set of mappings between peer ontologies. In addition to the schema mapping between peer ontologies, there are axioms to give constraints to classes and properties. We propose a set of rules to build graphs for the axioms. Because the axioms have different properties, the generated graphs are classified into four sets. In each peer, its RDF/OWL query languages can support regular expressions. If it wants to be transitive along semantic paths in peer knowledge management systems, we must rewrite conjunctive and disjunctive queries between peers. Because conjunctive queries are well-understood, we focus on a novel algorithm to rewrite disjunctive queries along semantic paths based on the graphs. For all atoms of a disjunctive query, we consider its union as a set and find the maximum rewritings over peers through a graphical way. Finally we do extensive simulation experiments. The simulation results show our algorithm can generate more rewritings than the naive rewriting algorithm at each distance. © 2007 IEEE.

关键词： Ontology

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A Semi-supervised Bilingual Lexicon Induction Method for Distant Language Pairs Based on Bidirectional Adversarial Model 12

A Semi-supervised Bilingual Lexicon Induction Method for Dis...

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1.th IEEE International Conference on Big knowledge, ICBK 2021

作者： Zhi, Wenwu Zhang, Yuhong Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Ministry of Education !!!School of Computer Science and Information Engineering @@@Hefei University of Technology Hefei China

ISBN: (纸本)9781665438582

Bilingual lexicon induction (BLI) can transfer knowledgefrom well- to under- resourced language, and has been widelyapplied to various NLP tasks. Recent work on BLI is projection-based that learns a mapping to connect source and target embedding spaces, with the isomorphism assumption. Unfortunately, the isomorphism assumption doesn't hold gener-ally, especially in typologically distant language pairs. Moreover, without supervised signals guiding, the training will further com-plicates BLI, making the performance of unsupervised methods unsatisfactory. To broke the restrict of isomorphism, we propose a semi-supervised method for distant BLI tasks, named A Semi-supervised Bilingual Lexicon Induction method in Latent Space based on Bidirectional Adversarial Model. First, two latent spaces are learned by two autoencoders for source and target domain independently to weaken the constraint of isomorphism in the embedding spaces. Then we add a few pairs of dictionary to learn the initial mapping to connect the Latent Space. Last, based on initial mapping, Cycle-Consistency is combined with Distance constraint constraint to maintain the geometry structure of both embedding spaces stable in the learning of bi-direction mapping based on adversarial model. By conducting extensive experiments, our method gets state-of-the-art results on most language pairs, especially with significant improvements on distant language pairs. © 2021.IEEE.

关键词： Mapping

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SGSH: Stimulate Large Language Models with Skeleton Heuristics for knowledge Base Question Generation

SGSH: Stimulate Large Language Models with Skeleton Heuristi...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Guo, Shasha Liao, Lizi Zhang, Jing Wang, Yanling Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Singapore Management University Singapore Zhongguancun Laboratory China

ISBN: (纸本)9798891761193

knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of our study. In this work, we propose SGSH - a simple and effective framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework incorporates "skeleton heuristics", which provides more fine-grained guidance associated with each input to stimulate LLMs to generate optimal questions, encompassing essential elements like the question phrase and the auxiliary verb. More specifically, we devise an automatic data construction strategy leveraging ChatGPT to construct a skeleton training dataset, based on which we employ a soft prompting approach to train a BART model dedicated to generating the skeleton associated with each input. Subsequently, skeleton heuristics are encoded into the prompt to incentivize GPT-3.5 to generate desired questions. Extensive experiments demonstrate that SGSH derives the new state-of-the-art performance on the KBQG tasks. The code is now available on Github. © 2024 Association for Computational Linguistics.

关键词： knowledge based systems

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Research on common issues for relationship between mechanism degree of freedom,driving link and executive link

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Science China(Technological Sciences) 2009年第4期52卷 966-974页

作者： WANG AiLun College of Mechanical & Electrical engineering, Central South University, Changsha 41.083, China {1. laboratory of Modern Complex Equipment Design and Extreme Manufacturing, ministry of education, Changsha 41.083, China 1. College of Mechanical & Electrical Engineering Central South University Changsha 410083 China 2. Key Laboratory of Modern Complex Equipment Design and Extreme Manufacturing Ministry of Education Changsha 410083 China

During engineering practice and theoretical research, the mechanism degree of freedom is sometimes unequal to the number of the driving link, which is unable to be explained according to the classic theory of the mechanism degree of freedom. To solve this problem, the duality description for operating laws of mechanism is introduced based on the concepts of duality. The Junction-Structure Equation is established for the port descriptions of general ideal mechanism and the functional description for the mechanism degree of freedom is described by utilization of bond graph theory. The force-transfer de- gree of freedom, i.e. the antithesis concept of movement-transfer degree of freedom, is introduced and defined based on the analysis of energy transfer as well as the analysis of signal causality. By utiliza- tion of structural description, functional description and duality description for the equation of degree of freedom, the relationship between the movement-transfer degree of freedom and force-transfer de- gree of freedom is further explored and analyzed. Thus, the relationship between the mechanism de- gree of freedom upon the driving link and the executive link is described generally. Finally, some typical relationships between the mechanism degree of freedom, the driving link and the executive link are analyzed.

关键词： duality bond graph Junction-Structure Equation driving link executive link degree of freedom

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