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8th Asia-Pacific Web Conference, APWeb 2006: Frontiers of WWW Research and Development

作者： Zhang, Cong-Le Huang, Sheng Xue, Gui-Rong Yu, Yong Apex Data and Knowledge Management Lab. Department of Computer Science and Engineering Shanghai JiaoTong University Shanghai 200030 China

ISBN: (纸本)3540311424

Since we can hardly get semantics from the low-level features of the image, it is much more difficult to analyze the image than textual information on the Web. Traditionally, textual information around the image is used to represent the high-level features of the image. We argue that such "flat" representation can not describe images well. In this paper, Hierarchical Representation (HR) and HR-Tree are proposed for image description. Salient phrases in HR-Tree are further to distinguish this image with others sharing the same ancestor concepts. First, we design a method to extract the salient phrases for the images in data records. Then HR-Trees are built using these phrases. Finally, new hierarchical clustering algorithm based on HR-Tree is proposed for users' browsing conveniently. We demonstrate some HR-Trees and clustering results in experimental section. These results illustrate the advantages of our methods. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Data mining

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Emergent semantics from folksonomies: A quantitative study

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Journal on Data Semantics VI - Special Issue on Emergent Semantics

作者： Zhang, Lei Wu, Xian Yu, Yong APEX Data and Knowledge Management Lab. Department of Computer Science and Engineering Shanghai JiaoTong University Shanghai 200030 China

ISBN: (纸本)3540367128

Defining and using ontology to annotate web resources with semantic markups is generally perceived as the primary way to implement the vision of the Semantic Web. The ontology provides a shared and machine understandable semantics for web resources that agents and applications can utilize. This top-down approach (in the sense that an ontology is defined first on top of existing web resources and then used later to markup them), however, has a high barrier to entry and is difficult to scale up. In this paper, we investigate using a bottom-up approach for semantically annotating web resources as supported by the now widely popular social bookmarks services on the web where users can annotate and categorize web resources using "tags" freely choosen by the user without any pre-existing global semantic model. This kind of informal social categories is coined as "folksonomies". We show how global semantics can be statistically inferred from the folksonomies to semantically annotate the web resources. The global semantic model also disambiguate the tags and group synonymous tags together. Finally, we show that there indeed are hierarchical relations among the emerged concepts in the folksonomy and it is plausible to further identify them if we use more advanced probabilistic models. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Semantics

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Web of data and web of entities: Identity and reference in interlinked data in the semantic web

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Philosophy and Technology 2012年第1期25卷 5-26页

作者： Bouquet, Paolo Stoermer, Heiko Vignolo, Massimiliano Department of Information Engineering and Computer Science University of Trento Trento Italy Fondazione Bruno Kessler Data Knowledge Management Unit Trento Trento Italy

Using web standards, such as uniform resource identifiers (URIs), XML and HTTP, for naming and describing resources which are not information objects is the key difference between the Web as we know it today and the Semantic Web. Naming and interlinking this type of resources by HTTP URIs (instead of individual constants in a formal language) is the key feature which distinguishes traditional knowledge representation from web-scale knowledge representation. However, this use of URIs brought back attention to the old philosophical problem of identity and reference in a new form. In this paper, we analyze the new version of the problem, provide a formal model for dealing with it when interlinking knowledge on the Web, and argue for the need of a distinction between the use of URIs for describing and accessing resources, and the use of URIs for fixing the reference. We show that in the current practice of linking data these roles are not clearly distinguished, and that this fact may cause unwanted effects and prevent some basic forms of data integration. We also discuss the role of an entity name system as a potential piece of infrastructure for fixing the reference in the Semantic Web. © 2011 Springer-Verlag.

关键词： Identity Linked data Reference Resource Semantic Web

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Local methods for estimating SimRank score

Local methods for estimating SimRank score

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12th International Asia Pacific Web Conference, APWeb 2010

作者： Jia, Xu Liu, Hongyan Zou, Li He, Jun Du, Xiaoyong Cai, Yuanzhe Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China 100872 China Department of Management Science and Engineering Tsinghua University 100084 China Department of Computer Science University of Texas Arlington TX 76019 United States

ISBN: (纸本)9780769540122

SimRank is a well known algorithm which conducts link analysis to measure similarity between each pair of nodes (nodepair). But it suffers from high computational cost, limiting its usage in large-scale datasets. Moreover, Links between nodes are changing over time. It may be desirable to quickly approximate the similarity score between certain nodepair without performing a large-scale computation on the entire graph. In our approach we propose a method to efficiently estimate the similarity score using only a small subgraph of the entire graph. We call this novel algorithm "Local-SimRank". The experimental results conducted on real datasets and synthetic dataset show that our algorithm efficiently produces good approximations to the global SimRank scores. Meanwhile, we prove that the Local-SimRank score LS(a, b) is always less than original SimRank score S(a, b) mathematically. © 2010 IEEE.

关键词： Approximation algorithms

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York University at CLEF eHealth 2015: Medical document retrieval 16

York University at CLEF eHealth 2015: Medical document retri...

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16th Conference and Labs of the Evaluation Forum, CLEF 2015

作者： Ghoddousi, Andia Huang, Jimmy Xiangji Information Retrieval and Knowledge Management Research Lab Department of Computer Science and Engineering School of Information Technology York University Canada

This paper presents the results for task 2 of ShAre/CLEF 2015 eHealth Evaluation Lab. We use BM25 as our base normalization method and Pseudo Relevance Feedback to retrieve information regarding patients' health and find the best results to expand the efficiency of information retrieval system. Participants in task 2 are provided with a collection of datasets focused on health web pages. We used queries and submitted 10 runs in TREC style. The runs include the top1000 documents returned for each query. The objective of task 2 is to evaluate the effectiveness of information retrieval system and develop search engines for searching on the web looking for health related documents[1].

关键词： Information retrieval

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DOGHED: A template-based generator for multimodal dialog systems targeting heterogeneous devices

DOGHED: A template-based generator for multimodal dialog sys...

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2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL 2003

作者： Channarukul, Songsak McRoy, Susan W. Ali, Syed S. Natural Language and Knowledge Representation Research Group Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee United States

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ANFIS-based Multilayered Algorithm for Botnet Detection

ANFIS-based Multilayered Algorithm for Botnet Detection

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2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Thamaraimanalan, T. Venkatesan, C. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Hkbk College of Engineering Department of Electronics and Communication Engineering Bangalore India Knowledge Institute of Technology Department of Computer Science and Business System Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350337426

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection. © 2023 IEEE.

关键词： Feature extraction

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Search in imperfect information games using online Monte Carlo counterfactual regret minimization 28

Search in imperfect information games using online Monte Car...

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28th AAAI Conference on Artificial Intelligence, AAAI 2014

作者： Lanctot, Marc Lis, Viliam Bowling, Michael Department of Knowledge Engineering Maastricht University Netherlands Department of Computer Science FEE Czech Technical University Prague Czech Republic Department of Computing Science University of Alberta Canada

ISBN: (纸本)9781577356653

Online search in games has always been a core interest of artif icial intel1igencc Advances made in search for perfect inform ation games (such as Chess. Checkers, Go. and Backgamm on) have led to Al capable of defeating the world's top hum an experts. Search in imperfect information games (such as Poker, Bridge, and Skat) is significantly more challengi ng due to the complexities introduced by hidden information. In this paper. we present Online Outcome Sampling (OOS). the first imperfect information search algorithm that is guara nteed to converge to an equilibrium strategy in two-player zero-sum games. We show that OOS avoids common probl ems encountered by existing search algorithms and we exp erimentally evaluate its convergence rate and practical perf ormance against benchmark strategies in Liar's Dice and a variant of Goofspiel. We show that unlike with Information Set Monte Carlo Tree Search (ISMCTS) the exploitabilily of the strategies produced by OOS decreases as the amount of search time increases. In practice. OOS performs as well as ISMCTS in head-to-head play while producing strategies with lower exploitability given the same search time. © Copyright 2014. Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Monte Carlo methods

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ECG Signal Feature Extraction and SVM Classifier Based Cardiac Arrhythmia Detection 2

ECG Signal Feature Extraction and SVM Classifier Based Cardi...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Venkatesan, C. Thamaraimanalan, T. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350397635

In a variety of medical applications, electrocardiogram (ECG) records are essential for the identification of cardiovascular disorders. Cardiovascular issues such cardiac arrhythmias and coronary heart disease (CHD) are among the most prevalent and can result in cardiac arrest or sudden cardiac death. This study employs ECG signal preprocessing and feature extraction to identify cardiac arrhythmias and evaluate CHD risk. This research stresses the use of a Support Vector Machine (SVM) classifier for cardiac arrhythmia identification after ECG signal preprocessing. The preprocessed ECG signal is then subjected to arrhythmic beat classification to find anomalies. Extracted R-peaks from the ECG signal are divided into normal and arrhythmic risk subjects using the SVM classification-based approach for abnormality identification. When compared to other similar classifiers, the K-Nearest Neighbor (KNN) classifier offers the highest classification accuracy of 97.5%. © 2023 IEEE.

关键词： Electrocardiograms

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A fast two-stage algorithm for computing SimRank and its extensions

A fast two-stage algorithm for computing SimRank and its ext...

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1st International Workshop on Graph Database, IWGD 2010, 1st International Workshop on Advanced Techniques on XML Data Management, XMLDM 2010, 2nd International Workshop on WCMT 2010, Held in Conj. with WAIM 2010

作者： Jia, Xu Liu, Hongyan Zou, Li He, Jun Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China 100872 China Department of Management Science and Engineering Tsinghua University 100084 China

ISBN: (纸本)3642167195

Similarity estimation can be used in many applications such as recommender system, cluster analysis, information retrieval and link prediction. SimRank is a famous algorithm to measure objects' similarities based on link structure. We observe that if one node has no in-link, similarity score between this node and any of the others is always zero. Based on this observation, we propose a new algorithm, fast two-stage SimRank (F2S-SimRank), which can avoid storing unnecessary zeros and can accelerate the computation without accuracy loss. Under the circumstance of no accuracy loss, this algorithm uses less computation time and occupies less main memory. Experiments conducted on real and synthetic datasets demonstrate the effectiveness and efficiency of our F2S-SimRank. © 2010 Springer-Verlag.

关键词： Cluster analysis

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