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2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Zhou, Jiantao Liang, Rong Wang, Yan College of Computer Science Inner Mongolia University Hohhot China Key Laboratory of Data Mining and Knowledge Engineering Hohhot China

ISBN: (纸本)9781457703713

Recently research on modeling methods of complicated processes under complex network environments has become a focus in workflow field. Now cloud computing environment provides a specific application background for the interaction and collaboration of workflows. In this paper, workflow processes were modeled as Semantics Constrained Net (SCN), which is one kind of extended Petri net. Then net synthesis mechanism constrained by semantics was researched, including synthesis rules and liveness proven. And a scalable solution of complex workflow process modeling in cloud was proposed. An instance of e-business was given to describe cooperative workflow under cloud computing environment. © 2011 IEEE.

关键词： Semantics

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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Identifying Scientific and Technical“Unicorns”

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Journal of data and Information Science 2021年第2期6卷 96-115页

作者： Lucy L.Xu Miao Qi Fred Y.Ye Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China University Library Nantong UniversityNantong 226019China

Purpose:Using the metaphor of"unicorn,"we identify the scientific papers and technical patents characterized by the informetric feature of very high citations in the first ten years after publishing,which may provide a new pattern to understand very high impact works in science and ***/methodology/approach:When we set CT as the total citations of papers or patents in the first ten years after publication,with CT≥5,000 for scientific"unicorn"and CT≥500 for technical"unicorn,"we have an absolute standard for identifying scientific and technical"unicorn"***:We identify 165 scientific"unicorns"in 14,301,875 WoS papers and 224 technical"unicorns"in 13,728,950 DII patents during 2001–*** 50%of"unicorns"belong to biomedicine,in which selected cases are individually *** rare"unicorns"increase following linear model,the fitting data show 95%confidence with the RMSE of scientific"unicorn"is 0.2127 while the RMSE of technical"unicorn"is *** limitations:A"unicorn"is a pure quantitative consideration without concerning its quality,and"potential unicorns"as CT≤5,000 for papers and CT≤500 for patents are left in future *** implications:Scientific and technical"unicorns"provide a new pattern to understand high-impact works in science and ***"unicorn"pattern supplies a concise approach to identify very high-impact scientific papers and technical ***/value:The"unicorn"pattern supplies a concise approach to identify very high impact scientific papers and technical patents.

关键词： Unicorn Scientific paper Technical patent Citation analysis Patent analysis

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Efficient SPARQL query processing in mapreduce through data partitioning and indexing

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14th Asia Pacific Web Technology Conference, APWeb 2012

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 data. 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 data in MapReduce. In this paper, we investigate RDF data partitioning technique that provides effective indexing schemes to support efficient SPARQL query processing in MapReduce. Our extensive experiments over a huge real-life RDF dataset show the performance of the proposed partitioning and indexing schemes for efficient SPARQL query processing. © 2012 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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2013 International Forum on Computer and Information Technology, IFCIT 2013

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. © (2014) Trans Tech Publications, Switzerland.

关键词： Virtual reality

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Zask: A case study on online collaborative learning

Zask: A case study on online collaborative learning

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10th IASTED International Conference on Web-Based Education, WBE 2013

作者： Wang, Xiaoyan Chen, Jinchuan Du, Xiaoyong School of Information Renmin University of China Beijing China School of Information and Electrical Engineering Ludong University Shandong China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

ISBN: (纸本)9780889869431

Modern educational theories, such as collaborative learning, constructivism and inquiry learning, have achieved many successes in real-world applications. Especially, with the development of information technologies, there have been several online collaborative learning platforms in practice. Unfortunately, as these platforms are either too complicated or too expensive, none of them are suitable for us in the practice of STEM+. Moreover, most of these platforms are in English, while we are using Chinese as our teaching language. Using an online collaborative learning platform (OCLP) named Zask, this paper reported our practice in online collaborative learning on course Introduction to database System. According to the data collected from the first round of our practice, it shows that users' active participations in Zask could benefit for both teaching and learning, and then provide positive effects in education.

关键词： Teaching

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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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Using ontology to enhance collaborative recommendation based on community

Using ontology to enhance collaborative recommendation based...

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9th International Conference on Web-Age Information Management, WAIM 2008

作者： Yu, Li School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China

ISBN: (纸本)9780769531854

Collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. It is not adapted to multi-interest or title recommendation for the 'general neighbourhood' problem which is analyzed in this paper. Based on it, collaborative filtering recommendation based on community is presented by introducing the concept 'community neighbourhood' in the paper. Unfortunately, it results into severer sparsity problem which makes heavy effect on its performance. In order to overcome it, an ontological A-priori score is used to infer user preference and to pre-fill null rating first. After pre-filling using the ontology method, then collaborative filtering based on community is executed based on a dense rating matrix. The experiment shows that collaborative filtering based on community makes generally better performance than traditional method when data is not very sparse, and ontology method can truly enhance collaborative filtering based on community since the sparsity is overcame. © 2008 IEEE.

关键词： Collaborative filtering

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Improve top-K recommendation by extending review analysis

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14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Zhu, Qing Xing, Zhe Liang, Jingfan School of Information Renmin University of China Beijing 100872 China Key Laboratory for Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing 100872 China

ISBN: (纸本)9783642292521

The Web has become the popular place for people to purchase product and acquire services, so collaborative filtering is one of the most important algorithms applied in e-commerce recommendation systems. Unfortunately, it is widely recognized that the traditional recommendation methods are inefficient when the user rating data is extremely sparse. In order to overcome the limitations, good recommendation tools are needed to help Web customers determine the products and satisfaction services. In this paper, we propose a multi-dimensional adaptive recommendation algorithm by extending opinion analysis to improve top-k recommendation. In the first step, the novel algorithm that uses extened opinion analysis, creatively combines three dimensional recommendation models: user-based, item-based and opinion-based collaborative filtering. It successfully integrates opinion mining technology with collaborative filtering algorithm. In the second step, we configured the dynamic measurement would help us determine the weight of three dimensions: user-based, item-based and opinion-based analysis, and hence get the final prediction result. The experimental results show that multi-dimensional recommendation can effectively alleviate the dataset sparsity problem and achieve better prediction accuracy compared to other traditional collaborative recommendation algorithms. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Collaborative filtering

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EEG-Based Joint Semi-supervised Learning for Major Depressive Disorder Detection 26th

EEG-Based Joint Semi-supervised Learning for Major Depressi...

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Chen, Tao Zheng, Tong Shi, Jinlong Guo, Yanrong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783031376597

Major Depressive Disorder (MDD) has been a major mental disease in recent years, imposing huge negative impacts on both our society and individuals. The current clinical MDD detection methods, such as self-report scales and interviews, often suffer from their subjectiveness. In contrast, electroencephalography (EEG) signals reflect brain activity and can be used as an effective information source for MDD detection. However, the learning-based MDD detection methods with EEG data usually neglect the valuable information contained in the unlabeled data, therefore possibly suffering from the label scarcity issue. In this context, we propose a simple EEG-based Joint Semi-supervised Learning (JSL) model for the MDD detection task, which fully leverages the information from unlabeled data. Specifically, by equipping the unlabeled samples with the intermediately predicted pseudo labels, the accurate position of the class-specific centroids in the feature space can be gradually refined. Based on that, the auxiliary labels based on a heuristic centroid-based classifier can be obtained, which plays an effective complementary role in predicting the sample labels. The experiments on a public dataset demonstrate the effectivenes s of our method, showing an obvious performance gain via using the proposed semi-supervised strategy. © 2023, Springer Nature Switzerland AG.

关键词： Electroencephalography

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