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International Conference on Hybrid Intelligent Systems (HIS)

作者： Nandita Tripathi Michael Oakes Stefan Wermter Department of Computing Engineering and Technology University of Sunderland Sunderland UK Institute for Knowledge Technology Department of Computer Science University of Hamburg Hamburg Germany

The volume and diversity of documents available in today's world is increasing daily. It is therefore difficult for a single classifier to efficiently handle multi-level categorization of such a varied document space. In this paper we analyse methods to enhance the efficiency of a single classifier for two-level classification by combining it with classifiers of other types. We use the maximum significance value as an indicator for the subspace of a test document. We represent the documents using the conditional significance vector which increases the distinction between classes within a subspace. Our experiments show that dividing a document space into different semantic subspaces increases the efficiency of such hybrid classifier combinations. Applying different types of classifiers on different subspaces substantially improves overall learning.

关键词： Vectors Support vector machine classification Semantics Accuracy Classification algorithms Training Niobium

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Towards energy saving and load balancing data aggregation for wireless sensor networks

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Information Technology Journal 2011年第2期10卷 409-415页

作者： Liang, Wei Xu, Jianbo Liu, Yuanyuan Key Laboratory of Knowledge Processing and Networked Manufacturing School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan 411201 China Department of Electrical Engineering Guangdong Songshan Polytechnic College Shaoguang 512126 China

In this study, we focus on the development of energy efficient and achievable load balancing mechanisms for wireless sensor networks. Due to resource constraint and tremendous amount of sensors, one possible way of achieving maximum lifetime of the network is applying data aggregation on sensor data. However, existing approaches introduce significant computation and control overheads that often not suitable for sensor networks applications. In view of this, we propose an in-network data aggregation scheme by exploiting the inherent spatial correlation of the sensed data. Each sensor multiplies its reading with a random coefficient and sends the product to the next hop to calculate a weighted sum of all the massage. Instead of receiving individual sensor readings, the sink will receive all weighted sum and restore the original data. By doing so, each node only performs one addition and one multiplication in order to compute one weighted sum and consume the same amount of energy. Simulation results demonstrate that the proposed scheme is efficient and outperforms existing schemes in terms of energy gain and network lifetime. © 2011 Asian Network for Scientific Information.

关键词： Energy efficiency

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A FCA-based approach for enriching users' knowledge base

A FCA-based approach for enriching users' knowledge base

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IEEE International Conference on Granular Computing (GRC)

作者： Eung-Hee Kim Hong-Gee Kim Suk-Hyung Hwang Biomedical Knowledge Engineering Laboratory Seoul National University Seoul South Korea Department of Computer Science and Engineering Sunmoon University Asan-si South Korea

Despite of the various benefits obtainable from Formal Concept Analysis (FCA) in knowledge base construction, FCA-based approaches are not enough to help an expert enrich his knowledge. This is because they provide only the clusters constructed with user-defined knowledge and super-sub relation between the clusters. In this paper, we propose an approach that provides a user with a guideline by suggesting undiscovered knowledge in the form of predicates. This approach firstly generates a set of candidate predicates by analyzing a pre-defined predicate by users. Second, it discards unqualified ones from the set of candidate predicates using a filtering method dealing with two criteria, uniqueness and support. The qualified candidate predicates are suggested, and selected by the user, and finally, his knowledge is enriched by merging the selected predicates with pre-defined ones.

关键词： Context Lattices Terminology knowledge based systems Guidelines Educational institutions knowledge engineering

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Reliable power control for secondary users based on distributed measurements

Reliable power control for secondary users based on distribu...

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IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN

作者： Gitte Vanwinckelen Martijn Van Otterlo Kurt Driessens Sofie Pollin Department of Computer Science Katholieke Universiteit Leuven Belgium Department of Knowledge Engineering University of Maastricht Netherlands Inter University Micro Electronics Center Leuven Belgium

An important aspect of spectrum sharing is reliable protection of licensed, primary, users from interference by unlicensed, secondary, users. In this paper we investigate the reliability of the iterative power adjustment algorithm proposed by Pollin, Adams and Bahai (2008). The goal of this transmission power control algorithm is to allow a static secondary transmitter to maximize its power without interfering with primary users. A distributed flooding algorithm is used to detect primary users and estimate the distance to the primary propagation contour. The secondary transmitter makes a local channel estimation with a moving least squares algorithm to average out noise. The metric used to estimate interference is the propagation contour-contour distance between the secondary and primary transmitters. In our first contribution we investigate the reliability of this metric by computing the location probability, a new FCC proposed metric for configuring Digital Terrestrial Television networks. We show that the propagation contour-contour distance is correlated with the location probability. In a second contribution we make the flooding algorithm more cost efficient by reducing communication. We then study the influence of the number of flooding messages on the performance of the iterative power adjustment algorithm in terms of location probability and number of iterations.

关键词： Fading

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The politics of comments : Predicting political orientation of news stories with commenters' sentiment patterns 11

The politics of comments : Predicting political orientation ...

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ACM 2011 Conference on computer Supported Cooperative Work, CSCW 2011

作者： Park, Souneil Ko, Minsam Kim, Jungwoo Liu, Ying Song, Junehwa Department of Computer Science Korea Advanced Institute of Science and Technology Daejeon 305-701 Korea Republic of Department of Knowledge Service Engineering Korea Advanced Institute of Science and Technology Daejeon 305-701 Korea Republic of

ISBN: (纸本)9781450305563

Political views frequently conflict in the coverage of contentious political issues, potentially causing serious social problems. We present a novel social annotation analysis approach for identification of news articles' political orientation. The approach focuses on the behavior of individual commenters. It uncovers commenters' sentiment patterns towards political news articles, and predicts the political orientation from the sentiments expressed in the comments. It takes advantage of commenters' participation as well as their knowledge and intelligence condensed in the sentiment of comments, thereby greatly reduces the high complexity of political view identification. We conduct extensive study on commenters' behaviors, and discover predictive commenters showing a high degree of regularity in their sentiment patterns. We develop and evaluate sentiment pattern-based methods for political view identification. Copyright 2011 ACM.

关键词： comments political news sentiment pattern political view identification commenters

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Towards Optimal Resource Provisioning for Running MapReduce Programs in Public Clouds

Towards Optimal Resource Provisioning for Running MapReduce ...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Fengguang Tian Keke Chen Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Department of Computer Science and Engineering Wright State University Dayton OH USA

Running MapReduce programs in the public cloud introduces the important problem: how to optimize resource provisioning to minimize the financial charge for a specific job? In this paper, we study the whole process of MapReduce processing and build up a cost function that explicitly models the relationship between the amount of input data, the available system resources (Map and Reduce slots), and the complexity of the Reduce function for the target MapReduce job. The model parameters can be learned from test runs with a small number of nodes. Based on this cost model, we can solve a number of decision problems, such as the optimal amount of resources that can minimize the financial cost with a time deadline or minimize the time under certain financial budget. Experimental results show that this cost model performs well on tested MapReduce programs.

关键词： Complexity theory Cloud computing Optimization Data models Analytical models Mathematical model Programming

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Multi-Label Classification based on Label-Specific Features

Multi-Label Classification based on Label-Specific Features

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2011年全国理论计算机科学学术年会

作者： QU Huaqiao ZHANG Shichao LIU Huawen ZHAO Jianmin Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Department of Computer Science Zhejiang Normal University Jinhua 321004 Department of Computer Science Zhejiang Normal University Jinhua 321004 China Department of Computer Science Zhejiang Normal University Jinhua 321004 China

lti-label learning aims at predicting a proper label set for each unseen *** instance in the dataset is associated with a set of predefined ***-label learning approaches frequently used choose identical feature set to...

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A trust model for capability delegation in federated policy systems

A trust model for capability delegation in federated policy ...

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International Conference on Risks and Security of Internet and Systems, CRiSIS

作者： Kevin Feeney Simon N. Foley Rob Brennan FAME and Knowledge & Data Engineering Group Intelligent Systems Laboratory Trinity College Dublin Ireland FAME and Department of Computer Science University College Cork Cork Ireland

Federated policy systems are required to support the emergent complexity and organizational heterogeneity of modern Internet service delivery. This paper presents a distributed policy management approach which utilizes a flexible, tree-based capability authority model to partition and delegate federated capabilities or services. A trust management model and a delegation logic is defined which supports secure decentralized policy reasoning and addresses performance overheads due to distributed rule evaluation, threats from malformed or malicious federated principals and allows flexibility with respect to delegation chain reduction or capability authority re-partitioning. The system is evaluated through a security analysis and a prototype implementation of a federated policy engineering framework based on this logic is described. This framework is based on public key certificates and an extension to the Keynote Trust Management language. It provides practical management services such as key discovery and certificate revocation in addition to the core capability delegation function.

关键词： Information services Public key Internet Semantics Authorization Web and internet services

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Using Folksonomy for Building User Preference List

Using Folksonomy for Building User Preference List

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IEEE International Symposium on Parallel and Distributed Processing with Applications Workshops (ISPAW)

作者： Harshit Kumar Pil Seong Park Hong Gee Kim Department of Computer Science University of Suwon South Korea Seoul National University South Korea BioMedical Knowledge Engineering Laboratory Seoul National University South Korea

In this work, we use folksonomies for building user preference list (UPL) based on user's search history. A UPL is an indispensable source of knowledge which can be exploited by intelligent systems for query recommendation, personalized search, and web search result ranking etc. A UPL consist of list of concepts, and their weights, clustered together using agglomerative clustering by employing Google Similarity Distance. We show how to design and implement such a system in practice and visualize the UPL which aids in finding interesting relationships between terms and detect outliers, if any. The experiment reveals that UPL not only captures user interests but also its context and results are very promising.

关键词： Clustering algorithms Classification algorithms Context Web search Partitioning algorithms Search engines Matrix converters

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Structure extraction from PDF-based book documents 11

Structure extraction from PDF-based book documents

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Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries

作者： Gao, Liangcai Tang, Zhi Lin, Xiaofan Liu, Ying Qiu, Ruiheng Wang, Yongtao Institute of Computer Science and Technology Peking University Beijing China Vobile Inc. 4699 Old Ironsides Drive Santa Clara CA United States Department of Knowledge Service Engineering KAIST Daejeon Korea Republic of State Key Laboratory of Digital Publishing Technology Peking University Founder Group Co. Ltd. Beijing China

ISBN: (纸本)9781450307444

Nowadays PDF documents have become a dominating knowledge repository for both the academia and industry largely because they are very convenient to print and exchange. However, the methods of automated structure information extraction are yet to be fully explored and the lack of effective methods hinders the information reuse of the PDF documents. To enhance the usability for PDF-formatted electronic books, we propose a novel computational framework to analyze the underlying physical structure and logical structure. The analysis is conducted at both page level and document level, including global typographies, reading order, logical elements, chapter/section hierarchy and metadata. Moreover, two characteristics of PDF-based books, i.e., style consistency in the whole book document and natural rendering order of PDF files, are fully exploited in this paper to improve the conventional image-based structure extraction methods. This paper employs the bipartite graph as a common structure for modeling various tasks, including reading order recovery, figure and caption association, and metadata extraction. Based on the graph representation, the optimal matching (OM) method is utilized to find the global optima in those tasks. Extensive benchmarking using real-world data validates the high efficiency and discrimination ability of the proposed method. © 2011 ACM.

关键词： Metadata

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