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ChinaGrid Annual Conference (ChinaGrid)

作者： Biao Fan Tao Liu He Hu Xiaoyong Du Ministry of Education Key Laboratory of Data Engineering and Knowledge Engineering China School of Information Renmin University of China Beijing China

This paper proposes a new method to cluster law texts based on referential relation of laws. We extract law entities (an entity represents a law) and their referential relation from law texts. Then SimRank algorithm is applied to calculate law entity's similarity through referential relation and law clustering is carried out based on the SimRank similarity. This is the first time to apply SimRank algorithm in the domain of Law and use it to carry out text clustering. Prototype and experiments show that our solution is feasible. We also publish the extracted data as Linked Law data with RDF data model, which forms the first open semantic web database in Law domain. Linked Law data enables user to access law data with rich data links and query web data by application interface of Semantic Web.

关键词： Clustering algorithms Heuristic algorithms databases Algorithm design and analysis Resource description framework data mining Semantics

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Reverse Nearest Neighbor Heat Maps: A Tool for Influence Exploration

Reverse Nearest Neighbor Heat Maps: A Tool for Influence Exp...

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IEEE International Conference on data engineering

作者： Yu Sun Rui Zhang Andy Yuan Xue Jianzhong Qi Xiaoyong Du Department of Computing and Information Systems University of Melbourne Renmin University of China and Key Laboratory of Data Engineering and Knowledge Engineering MOE

ISBN: (纸本)9781509020218

We study the problem of constructing a reverse nearest neighbor (RNN) heat map by finding the RNN set of every point in a two-dimensional space. Based on the RNN set of a point, we obtain a quantitative influence (i.e., heat) for the point. The heat map provides a global view on the influence distribution in the space, and hence supports exploratory analyses in many applications such as marketing and resource management. To construct such a heat map, we first reduce it to a problem called Region Coloring (RC), which divides the space into disjoint regions within which all the points have the same RNN set. We then propose a novel algorithm named CREST that efficiently solves the RC problem by labeling each region with the heat value of its containing points. In CREST, we propose innovative techniques to avoid processing expensive RNN queries and greatly reduce the number of region labeling operations. We perform detailed analyses on the complexity of CREST and lower bounds of the RC problem, and prove that CREST is asymptotically optimal in the worst case. Extensive experiments with both real and synthetic data sets demonstrate that CREST outperforms alternative algorithms by several orders of magnitude.

关键词： Heating Radio frequency Indexes

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Analysis of bandwagon and average hybrid attack model against trust-based recommender systems

Analysis of bandwagon and average hybrid attack model agains...

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2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Zhang, Fuguo School of Information and Technology Jiangxi University of Finance and Economics Nanchang China Institute of Information Resource Management Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9780769545448

Recommender systems have been accepted as a vital application on the web by offering product advice or information that users might be interested in. Despite its success, similarity-based collaborative filtering suffers from some significant limitations, such as scalability, sparsity and recommendation attack. Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations. However, trust-based recommender systems are also known to be vulnerable to profile infection attacks. Malicious users can inject a large number of biased profiles into such a system in order to make recommendations that favor or disfavor given items. In this paper, we propose a bandwagon and average hybrid attack model and analysis the effectiveness of the attack model against topic-level trust-based recommender algorithm. The results of our experiments conducted on well-known dataset show that the hybrid attack model is more effective than other attack models. © 2011 IEEE.

关键词： Recommender systems

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OLTP workloads on modern processor: Characterization and analysis

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Journal of Computational Information Systems 2008年第1期4卷 389-394页

作者： Liu, Dawei Qin, Biao Wang, Shan Gong, Weiwei School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 200872 China

This paper analysis of how OLTP workloads interact with modern processors and caches behavior. First, we extend TPC-C, the OLTP-oriented benchmark, to ETPC-C benchmark, for measuring the performance of main-memory database (MMDBMS) more precisely. As the performance of MMDBMS is not affected by disk I/O, it is more sensitive to cache usage. Then using ETPC-C benchmark, we investigated the behavior of caches and processors extensively. We find that the miss stall time is mostly spent on on-CPU-chip caches, that is, the first and second level cache misses are dominant. Furthermore, we find instruction cache (I-cache) stall time of on-CPU-chip is a major component to the memory stall time. The smaller the emulated users, the more proportion the I-cache stall time of on-CPU-chip contributes to the memory stall time. However, if employing index, the system under test (SUT) has more total I-cache stall time than the SUT without index at the same number of emulated users and data population. Another observation is that the SUT with index has a little more branch misprediction rate than the SUT without index in average. Finally, we find only the third level (L3) D-cache stall time rate increases with the number of users. This is because L3 D-cache miss incremental rate is the largest. Under TPC-and ETPC-evaluation, we find that for optimized database performance on modern computers, reducing instruction miss penalty is equally important to reducing data miss penalty;since they are conflict efforts, the best way is to have them balanced.

关键词： Program processors

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Records systems and information systems: Connecting in organizations 8th

Records systems and information systems: Connecting in organ...

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8th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2016

作者： Xie, Sherry L. Fan, Guanyan Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing100872 China Center for Electronic Records Management Research Beijing100872 China School of Information Resource Management Renmin University of China 59 Zhongguancun Ave Haidian District Beijing100872 China

ISBN: (纸本)9783319997001

The field of Information Systems (ISs) has long been recognized, so has Enterprise Information Systems (EISs), a field close to it. Long existing also in organizations or enterprises is the field of records management, now predominantly digital records management (DRM), which shares the many goals of ISs and EISs in supporting the operation and success of organizations. While the DRM field recognized rather early in its battle to digital records challenges that the need to establish formal collaborations with the ISs and EISs professions, it is still rare to spot today discussions regarding such collaborations in the general information and communication technology (ICT) literature. To help bridge the gap, this chapter introduces to the ISs and EISs professions one of the major developments of the international DRM field, that is, the records systems elaborated by the InterPARES (International Research on Permanent and Authentic Records in Electronic Systems) project, for the purpose of invoking further discussions. © Springer Nature Switzerland AG 2019.

关键词： Information systems

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Initializing convolutional filters with semantic features for text classification

Initializing convolutional filters with semantic features fo...

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2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Li, Shen Zhao, Zhe Liu, Tao Hu, Renfen Du, Xiaoyong Institute of Chinese Information Processing Beijing Normal University China UltraPower-BNU Joint Laboratory for Artificial Intelligence Beijing Normal University China College of Chinese Language and Culture Beijing Normal University China School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China

ISBN: (纸本)9781945626838

Convolutional Neural Networks (CNNs) are widely used in NLP tasks. This paper presents a novel weight initialization method to improve the CNNs for text classification. Instead of randomly initializing the convolutional filters, we encode semantic features into them, which helps the model focus on learning useful features at the beginning of the training. Experiments demonstrate the effectiveness of the initialization technique on seven text classification tasks, including sentiment analysis and topic classification. © 2017 Association for Computational Linguistics.

关键词： Sentiment analysis

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knowledge Representation and Reasoning for Complex Time Expression in Clinical Text

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data Intelligence 2022年第3期4卷 573-598页

作者： Danyang Hu Meng Wang Feng Gao Fangfang Xu Jinguang Gu School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration of the People’s Republic of ChinaBeijing 10038China Institute of Big Data Science and Engineering Wuhan University of Science and TechnologyWuhan 430065China

Temporal information is pervasive and crucial in medical records and other clinical text,as it formulates the development process of medical conditions and is vital for clinical decision ***,providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is *** order to capture complex temporal semantics in clinical text,we propose a novel Clinical Time Ontology(CTO)as an extension from OWL *** specifically,we identified eight timerelated problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time,cyclic time,irregular time,negations and other complex aspects of clinical ***,we extended Allen’s and TEO’s temporal relations and defined the relation concept description between complex and simple ***,we provided a formulaic and graphical presentation of complex time and complex time *** carried out empirical study on the expressiveness and usability of CTO using real-world healthcare ***,experiment results demonstrate that CTO could faithfully represent and reason over 93%of the temporal expressions,and it can cover a wider range of time-related classes in clinical domain.

关键词： Clinical text Temporal ontology Temporal relations OWL Negation of temporal relation

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Sentiment classification via L2-norm deep belief network 11

Sentiment classification via L2-norm deep belief network

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20th ACM Conference on Information and knowledge Management, CIKM'11

作者： Liu, Tao Li, Minghui Zhou, Shusen Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering MOE 100872 Beijing China School of Information Renmin University of China 100872 Beijing China Microsoft Asian Research and Development Group 100080 Beijing China Shenzhen Graduate School Harbin Institute of Technology 518055 Shenzhen China

ISBN: (纸本)9781450307178

Automatic analysis of sentiments expressed in large scale online reviews is very important for intelligent business applications. Sentiment classification is the most popular task of sentiment analysis, which is more challenging than traditional topic-based text classification. Basic features, such as vocabulary words, are not enough to classify sentiments well. Deep Belief Network (DBN) is introduced to discover more abstract features of sentiments. To capture full information of the features, large-size network can be constructed, but at the same time, large-size network tends to over fit the training data and even noise, which will reduce the generalization ability of the network. In this paper, L2-norm Deep Belief Network (L2DBN) is proposed, which uses L2-norm regularization to optimize the network parameters of DBN. L2DBN is first initialized by an unsupervised layer-wise training algorithm, and then fine-tuned by a supervised procedure. Network parameters are optimized using both classification loss and network complexity. Experimental results show that the proposed L2DBN outperforms the state-of-the-art method and the basic DBN on golden, noisy and heterogeneous datasets. © 2011 ACM.

关键词： Sentiment analysis

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Learning Group-Disentangled Representation for Interpretable Thoracic Pathologic Prediction

Learning Group-Disentangled Representation for Interpretable...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Hao Wu, Yirui Hu, Hexuan Lu, Hu Lai, Yong Wan, Shaohua Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources China College of Computer and Information Hohai University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Computer Science and Communication Engineering Jiangsu University China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

ISBN: (纸本)9781665468190

Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like 'black box' without explanations in both feature extraction and decision processes, leading to lack of clinical insights and high risk assessments. To aid deep learning in envisioning diseases with visual clues, we propose Representation Group-Disentangling Network (RGD-Net), which can completely disentangle feature space of input X-ray images into several independent feature groups, each corresponding to a specific disease. Taking several semantically related and labeled X-ray images as input, RGD-Net firstly extracts completely group-disentangled representations of diseases through Group-Disentangle Module, which applies group-swap and linking operations to construct latent space by enforcing semantic consistency of attributes. To prevent learning degenerate representations defined as shortcut problem, we further introduce adversarial constricts on mapping from features to diseases, thus avoiding model collapse with former free-form disentanglement. Experiments on chestxray-14 and ChestXpert datasets demonstrate that RGD-Net are effective in predicting diseases with remarkable advantages, which leverage potential factors contributing to different diseases, thus enhancing interpretability in working patterns of deep learning methods. © 2022 IEEE.

关键词： Forecasting

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Personalizing search results using hierarchical RNN with query-aware attention

arXiv

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arXiv 2019年

作者： Ge, Songwei Dou, Zhicheng Jiang, Zhengbao Nie, Jian-Yun Wen, Ji-Rong School of Information Renmin University of China DIRO Université de Montréal Beijing Key Laboratory of Big Data Management and Analysis Methods National Engineering Laboratory of Big Data System Software Beijing Institute of Technology Key Laboratory of Data Engineering and Knowledge Engineering MOE

Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training. Copyright © 2019, The Authors. All rights reserved.

关键词： Search engines

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