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A BAT in the lab: Experimental results of new link state routing protocol

A BAT in the lab: Experimental results of new link state rou...

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22nd international conference on Advanced Information Networking and Applications, AINA 2008

作者： Ikeda, Makoto De Marco, Giuseppe Barolli, Leonard Takizawa, Makoto 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka 811-0295 Japan Department of Engineering Toyota Technological Institute 2-12-1 Hisakata Tenpaku-ku Nagoya 468-8511 Japan Department of Information and Communication Engineering FIT 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka 811-0295 Japan Department of Computers and Systems Engineering Tokyo Denki University Ishizaka Hatoyama Saitama 350-0394 Japan

ISBN: (纸本)0769530958

In this paper, we analyze the common problems of link state routing protocols along with an evolution of the Optimized Link State Routing protocol, called BATMAN. BATMAN uses a weighted and auto-selective flooding which makes it a sort of gossip protocol. The flooding uses short control packets to make every mesh node aware of network topology. However, there is no topology information dissemination and no multi-point relaying node selection. While routing loops are easily avoided by this modified flooding mechanism, there are still problems about self-interference. Due the broadcast nature of the radio channel, traffic data can disturb control data, and localized control data may cause interference on neighboring control data as well. In fact there is a complicated interaction between MAC and routing protocols. We demonstrate this fact by extensive field tests carried out by our measurement framework based on statistical tests. In particular, we show that the so called hop horizon of multi-hop network can be due to the routing and MAC protocols. These analysis can be a useful hint for the design of more intelligent routing, or rather a less cross-layer dependence of protocols. © 2008 IEEE.

关键词： Routing protocols

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intelligent Dynamic XML Documents Clustering

Intelligent Dynamic XML Documents Clustering

引用

international conference on Advanced Information Networking and Applications (AINA)

作者： Laura Irina Rusu Wenny Rahayu David Taniar Department of Comp Sc & Comp Engineering La Trobe University Australia Clayton Monash University Australia

Clustering as an intelligent technique for mining XML documents has been utilised as an excellent way of grouping the documents by their content or structure. A main step in many distance based XML clustering algorithms is to calculate pair-wise distances between documents; naturally, a time-efficient technique requests the pair-wise distances to be determined in a timely manner. In case of dynamic XML documents, the amount of changes between versions cannot be predicted. Therefore, in case of clustered dynamic XML documents, if changes were little or if they affected only some of the clustered documents, recalculating pair-wise distances every time would be highly redundant. In this paper we propose a time-efficient technique to reassess pair- wise distances between clustered dynamic XML documents which change in time, without performing redundant calculations but considering the previously known distances and the set of changes which might have affected the documents versions.

关键词： XML Clustering algorithms data mining intelligent networks intelligent structures Association rules Information retrieval Deductive databases Distance measurement Extraterrestrial measurements

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A Communication-Efficient Distributed Clustering Algorithm for Sensor Networks

A Communication-Efficient Distributed Clustering Algorithm f...

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international conference on Advanced Information Networking and Applications Workshops (AINAW)

作者： Amirhosein Taherkordi Reza Mohammadi Frank Eliassen Department of Informatics University of Oslo Norway IT Engineering Department Tarbiat Moalem University Iran

Sensor networks usually generate continuous stream of data over time. Clustering sensor data as a core task of mining sensor data plays an essential role in analytical applications of sensor networks. Although several algorithms have been proposed to address the problem of distributed clustering, in the domain of sensor networks these algorithms face major new challenges such as limited communication bandwidth and constraints in power supply, and storage resources. Moreover, previous studies about clustering in sensor networks have mostly focused on clustering sensor nodes and designing better network topology for the purpose of energy conservation rather than clustering sensor data for future analytical purposes. In this paper a communication efficient distributed algorithm is proposed for clustering sensory data. This approach addresses the limited bandwidth issue through summarized transmissions. Furthermore communication efficiency of the algorithm contributes to reduced power consumption. Time efficiency of the algorithm is evaluated through simulation experiments and the results are presented.

关键词： Clustering algorithms Wireless sensor networks Sensor phenomena and characterization data mining data analysis Bandwidth intelligent sensors Biomedical monitoring Algorithm design and analysis Informatics

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A Networked Virtual Environment for Teaching Handwritten Characters Through Haptization of Human Motor Skills

A Networked Virtual Environment for Teaching Handwritten Cha...

引用

international conference on Advanced Information Networking and Applications (AINA)

作者： Hiroaki Nishino Akira Yamaguchi Tsuneo Kagawa Kouichi Utsumiya Department of Computer Science and Intelligent Systems Oita University Oita Japan

We present an approach to realize a networked on-line education system among physically separated participants targeted at teaching handwritten characters. In addition to visual and acoustic information, our system provides a haptic channel to intuitively learn remote instructor's fine motor skills through the sense of touch. The human touch sensation, however, is very sensitive for performance degradation caused by delays, packet losses, and jitters in the networked virtual reality (NVR) systems. Therefore, we show a method to efficiently share virtual space information by compensating for the performance degradation factors. We take a hybrid approach to implement both data consistency by client-server and scalability by peer-to-peer models. The handwritten character learning system is built on the hybrid architecture and realizes the participants to tightly share partners' haptic motions. We also describe an experiment conducted to validate the effectiveness of the haptic modality for teaching handwritten characters.

关键词： Virtual environment Education Humans Haptic interfaces Degradation Performance loss Jitter Virtual reality Scalability Peer to peer computing

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New Computational Intelligence Paradigm for Estimating the Software Project Effort

New Computational Intelligence Paradigm for Estimating the S...

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international conference on Advanced Information Networking and Applications Workshops (AINAW)

作者： Emad A. El-Sebakhy Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

Recently, there are numerous techniques have been proposed to forecast and identify the software development effort; such prediction has a prominent impact on the success of software development projects. The most common methods for estimating software development efforts that have been proposed in literature are: line of code (LOC)-based constructive cost model (COCOMO), function point- based regression model (FP), neural network model (NN), and case-based reasoning (CBR). Recent research has tended to focus on the use of function points (FPs) in estimating the software development efforts, however, a precise estimation should not only consider the FPs, which represent the size of the software, but should also include various elements of the development environment for its estimation. Therefore, the main benefit of this study is to design and analyze both function points and development environments of recent software development cases. Therefore, this paper presents a new intelligence paradigm scheme based on functional network to forecast that emphasizes on numerous software development elements. Both implementation and learning process are based on the utilization of functional networks as a new modeling scheme and investigate its efficiency as a software development estimation model for predicting the software development efforts.

关键词： Computational intelligence Programming Software maintenance Object oriented modeling Predictive models intelligent networks Software systems Neural networks data mining Artificial neural networks

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A BAT in the Lab: Experimental Results of New Link State Routing Protocol

A BAT in the Lab: Experimental Results of New Link State Rou...

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international conference on Advanced Information Networking and Applications (AINA)

作者： Makoto Ikeda Giuseppe De Marco Leonard Barolli Makoto Takizawa Graduate School of Engineering Fukuoka Institute of Technology (FIT) Higashi-Ku Fukuoka Japan Toyota Technological Institute Nagoya Japan FIT Higashi-Ku Fukuoka Japan Tokyo Denki University Hatoyama Saitama Japan

关键词： Routing protocols Radio control Floods Network topology Media Access Protocol Testing Relays Radio broadcasting Communication system traffic control Interference

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Look Ma, no hands: analyzing the monotonic feature abstraction for text classification 08

Look Ma, no hands: analyzing the monotonic feature abstracti...

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Proceedings of the 22nd international conference on Neural Information Processing Systems

作者： Doug Downey Oren Etzioni Electrical Engineering and Computer Science Department Northwestern University Evanston IL Turing Center Department of Computer Science and Engineering University of Washington Seattle WA

ISBN: (纸本)9781605609492

Is accurate classification possible in the absence of hand-labeled data? This paper introduces the Monotonic Feature (MF) abstraction—where the probability of class membership increases monotonically with the MF's value. The paper proves that when an MF is given, PAC learning is possible with no hand-labeled data under certain *** argue that MFs arise naturally in a broad range of textual classification applications. On the classic "20 Newsgroups" data set, a learner given an MF and unlabeled data achieves classification accuracy equal to that of a state-of-the-art semi-supervised learner relying on 160 hand-labeled examples. Even when MFs are not given as input, their presence or absence can be determined from a small amount of hand-labeled data, which yields a new semi-supervised learning method that reduces error by 15% on the 20 Newsgroups data.

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Unlabeled data: now it helps, now it doesn't 08

Unlabeled data: now it helps, now it doesn't

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Proceedings of the 22nd international conference on Neural Information Processing Systems

作者： Aarti Singh Robert D. Nowak Xiaojin Zhu Department of Electrical and Computer Engineering University of Wisconsin - Madison Madison WI Department of Computer Sciences University of Wisconsin - Madison Madison WI

ISBN: (纸本)9781605609492

Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performance of a learning task, in the sense that fewer labeled training data are needed to achieve a target error bound. However, in other situations unlabeled data do not seem to help. Recent attempts at theoretically characterizing SSL gains only provide a partial and sometimes apparently conflicting explanations of whether, and to what extent, unlabeled data can help. In this paper, we attempt to bridge the gap between the practice and theory of semi-supervised learning. We develop a finite sample analysis that characterizes the value of un-labeled data and quantifies the performance improvement of SSL compared to supervised learning. We show that there are large classes of problems for which SSL can significantly outperform supervised learning, in finite sample regimes and sometimes also in terms of error convergence rates.

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Human active learning 08

Human active learning

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Proceedings of the 22nd international conference on Neural Information Processing Systems

作者： Rui Castro Charles Kalish Robert Nowak Ruichen Qian Timothy Rogers Xiaojin Zhu Department of Electrical Engineering Columbia University. New York NY Department of Psychology University of Wisconsin-Madison Madison WI Department of Electrical and Computer Engineering University of Wisconsin-Madison Madison WI Department of Computer Sciences University of Wisconsin-Madison Madison WI

ISBN: (纸本)9781605609492

We investigate a topic at the interface of machine learning and cognitive science. Human active learning, where learners can actively query the world for information, is contrasted with passive learning from random examples. Furthermore, we compare human active learning performance with predictions from statistical learning theory. We conduct a series of human category learning experiments inspired by a machine learning task for which active and passive learning error bounds are well understood, and dramatically distinct. Our results indicate that humans are capable of actively selecting informative queries, and in doing so learn better and faster than if they are given random training data, as predicted by learning theory. However, the improvement over passive learning is not as dramatic as that achieved by machine active learning algorithms. To the best of our knowledge, this is the first quantitative study comparing human category learning in active versus passive settings.

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Partially observed Maximum Entropy Discrimination Markov Networks 08

Partially observed Maximum Entropy Discrimination Markov Net...

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Proceedings of the 22nd international conference on Neural Information Processing Systems

作者： Jun Zhu Eric P. Xing Bo Zhang State Key Lab of Intelligent Tech & Sys Tsinghua National TNList Lab Dept. Comp Sci & Tech Tsinghua University Beijing China School of Comp. Sci. Carnegie Mellon University Pittsburgh PA

ISBN: (纸本)9781605609492

learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unattainable fully annotated training data. While likelihood-based methods have been extensively explored, to our knowledge, learning structured prediction models with latent variables based on the max-margin principle remains largely an open problem. In this paper, we present a partially observed Maximum Entropy Discrimination Markov Network (PoMEN) model that attempts to combine the advantages of Bayesian and margin based paradigms for learning Markov networks from partially labeled data. PoMEN leads to an averaging prediction rule that resembles a Bayes predictor that is more robust to overfitting, but is also built on the desirable discriminative laws resemble those of the M3N. We develop an EM-style algorithm utilizing existing convex optimization algorithms for M3N as a subroutine. We demonstrate competent performance of PoMEN over existing methods on a real-world web data extraction task.

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