<?xml version="1.0" ?>
<rss version="2.0">
	<channel>
		<title><![CDATA[ Knowledge and Data Engineering, IEEE Transactions on - new TOC ]]></title>
		<link>http://ieeexplore.ieee.org</link>
		<description>TOC Alert for Publication# 69 </description>
		<year>2010</year>
		<month>February </month>
		<day>09</day>
		<item>
			<title><![CDATA[Cover1]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394982]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394982]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>c1</startPage>
			<endPage>c1</endPage>
			<fileSize>162</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[Cover2]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394983]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394983]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>c2</startPage>
			<endPage>c2</endPage>
			<fileSize>204</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4912203]]></link>
			<description><![CDATA[Feature Selection (FS) or Attribute Reduction techniques are employed for dimensionality reduction and aim to select a subset of the original features of a data set which are rich in the most useful information. The benefits of employing FS techniques include improved data visualization and transparency, a reduction in training and utilization times and potentially, improved prediction performance. Many approaches based on rough set theory up to now, have employed the dependency function, which is based on lower approximations as an evaluation step in the FS process. However, by examining only that information which is considered to be certain and ignoring the boundary region, or region of uncertainty, much useful information is lost. This paper examines a rough set FS technique which uses the information gathered from both the lower approximation dependency value and a distance metric which considers the number of objects in the boundary region and the distance of those objects from the lower approximation. The use of this measure in rough set feature selection can result in smaller subset sizes than those obtained using the dependency function alone. This demonstrates that there is much valuable information to be extracted from the boundary region. Experimental results are presented for both crisp and real-valued data and compared with two other FS techniques in terms of subset size, runtimes, and classification accuracy.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4912203]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>305</startPage>
			<endPage>317</endPage>
			<fileSize>2688</fileSize>
			<authors><![CDATA[Parthal&#x0E1;in, Neil;Shen, Qiang;Jensen, Richard;]]></authors>
		</item>
		<item>
			<title><![CDATA[A General Framework of Time-Variant Bandwidth Allocation in the Data Broadcasting Environment]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4803838]]></link>
			<description><![CDATA[Data broadcast is an advanced technique to realize large scalability and bandwidth utilization in a mobile computing environment. In this environment, the channel bandwidth of each channel is variant with time in real cases. However, traditional schemes do not consider time-variant bandwidth of each channel to schedule data items. Therefore, the above drawback degrades the performance in generating broadcast programs. In this paper, we address the problem of generating a broadcast program to disseminate data via multiple channels of time-variant bandwidth. In view of the characteristics of time-variant bandwidth, we propose an algorithm using adaptive allocation on time-variant bandwidth to generate the broadcast program to avoid the above drawback to minimize average waiting time. Experimental results show that our approach is able to generate the broadcast programs with high quality and is very efficient in a data broadcasting environment with the time-variant bandwidth.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4803838]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>318</startPage>
			<endPage>333</endPage>
			<fileSize>2610</fileSize>
			<authors><![CDATA[Chu, Chung-Hua;Hung, Hao-Ping;Chen, Ming-Syan;]]></authors>
		</item>
		<item>
			<title><![CDATA[Efficient Multidimensional Suppression for K-Anonymity]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4840348]]></link>
			<description><![CDATA[Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. One way to enable effective data mining while preserving privacy is to anonymize the data set that includes private information about subjects before being released for data mining. One way to anonymize data set is to manipulate its content so that the records adhere to k-anonymity. Two common manipulation techniques used to achieve k-anonymity of a data set are generalization and suppression. Generalization refers to replacing a value with a less specific but semantically consistent value, while suppression refers to not releasing a value at all. Generalization is more commonly applied in this domain since suppression may dramatically reduce the quality of the data mining results if not properly used. However, generalization presents a major drawback as it requires a manually generated domain hierarchy taxonomy for every quasi-identifier in the data set on which k-anonymity has to be performed. In this paper, we propose a new method for achieving k-anonymity named K-anonymity of Classification Trees Using Suppression (kACTUS). In kACTUS, efficient multidimensional suppression is performed, i.e., values are suppressed only on certain records depending on other attribute values, without the need for manually produced domain hierarchy trees. Thus, in kACTUS, we identify attributes that have less influence on the classification of the data records and suppress them if needed in order to comply with k-anonymity. The kACTUS method was evaluated on 10 separate data sets to evaluate its accuracy as compared to other k-anonymity generalization- and suppression-based methods. Encouraging results suggest that kACTUS' predictive performance is better than that of existing k-anonymity algorithms. Specifically, on average, the accuracies of TDS, TDR, and kADET are lower than kACTUS in 3.5, 3.3, and 1.9 percent, respectively, despite their u-
sage of manually defined domain trees. The accuracy gap is increased to 5.3, 4.3, and 3.1 percent, respectively, when no domain trees are used.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4840348]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>334</startPage>
			<endPage>347</endPage>
			<fileSize>4641</fileSize>
			<authors><![CDATA[Kisilevich, Slava;Rokach, Lior;Elovici, Yuval;Shapira, Bracha;]]></authors>
		</item>
		<item>
			<title><![CDATA[Beyond Redundancies: A Metric-Invariant Method for Unsupervised Feature Selection]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4815245]]></link>
			<description><![CDATA[A fundamental goal of unsupervised feature selection is denoising, which aims to identify and reduce noisy features that are not discriminative. Due to the lack of information about real classes, denoising is a challenging task. The noisy features can disturb the reasonable distance metric and result in unreasonable feature spaces, i.e., the feature spaces in which common clustering algorithms cannot effectively find real classes. To overcome the problem, we make a primary observation that the relevance of features is intrinsic and independent of any metric scaling on the feature space. This observation implies that feature selection should be invariant, at least to some extent, with respect to metric scaling. In this paper, we clarify the necessity of considering the metric invariance in unsupervised feature selection and propose a novel model incorporating metric invariance. Our proposed method is motivated by the following observations: if the statistic that guides the unsupervised feature selection process is invariant with respect to possible metric scaling, the solution of this model will also be invariant. Hence, if a metric-invariant model can distinguish discriminative features from noisy ones in a reasonable feature space, it will also work on the unreasonable counterpart transformed from the reasonable one by metric scaling. A theoretical justification of the metric invariance of our proposed model is given and the empirical evaluation demonstrates its promising performance.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4815245]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>348</startPage>
			<endPage>364</endPage>
			<fileSize>2380</fileSize>
			<authors><![CDATA[Hou, Yuexian;Zhang, Peng;Yan, Tingxu;Li, Wenjie;Song, Dawei;]]></authors>
		</item>
		<item>
			<title><![CDATA[Constrained Dimensionality Reduction Using a Mixed-Norm Penalty Function with Neural Networks]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4840349]]></link>
			<description><![CDATA[Reducing the dimensionality of a classification problem produces a more computationally-efficient system. Since the dimensionality of a classification problem is equivalent to the number of neurons in the first hidden layer of a network, this work shows how to eliminate neurons on that layer and simplify the problem. In the cases where the dimensionality cannot be reduced without some degradation in classification performance, we formulate and solve a constrained optimization problem that allows a trade-off between dimensionality and performance. We introduce a novel penalty function and combine it with bilevel optimization to solve the constrained problem. The performance of our method on synthetic and applied problems is superior to other known penalty functions such as weight decay, weight elimination, and Hoyer's function. An example of dimensionality reduction for hyperspectral image classification demonstrates the practicality of the new method. Finally, we show how the method can be extended to multilayer and multiclass neural network problems.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4840349]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>365</startPage>
			<endPage>380</endPage>
			<fileSize>3891</fileSize>
			<authors><![CDATA[Zeng, Huiwen;Trussell, H. J.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Ensemble Rough Hypercuboid Approach for Classifying Cancers]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4912198]]></link>
			<description><![CDATA[Cancer classification is the critical basis for patient-tailored therapy. Conventional histological analysis tends to be unreliable because different tumors may have similar appearance. The advances in microarray technology make individualized therapy possible. Various machine learning methods can be employed to classify cancer tissue samples based on microarray data. However, few methods can be elegantly adopted for generating accurate and reliable as well as biologically interpretable rules. In this paper, we introduce an approach for classifying cancers based on the principle of minimal rough fringe. For training rough hypercuboid classifiers from gene expression data sets, the method dynamically evaluates all available genes and sifts the genes with the smallest implicit regions as the dimensions of implicit hypercuboids. An unseen object is predicted to be a certain class if it falls within the corresponding class hypercuboid. Based upon the method, ensemble rough hypercuboid classifiers are subsequently constructed. Experimental results on some open cancer gene expression data sets show that the proposed method is capable of generating accurate and interpretable rules compared with some other machine learning methods. Hence, it is a feasible way of classifying cancer tissues in biomedical applications.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4912198]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>381</startPage>
			<endPage>391</endPage>
			<fileSize>1791</fileSize>
			<authors><![CDATA[Wei, Jin-Mao;Wang, Shu-Qin;Yuan, Xiao-Jie;]]></authors>
		</item>
		<item>
			<title><![CDATA[k-Anonymity in the Presence of External Databases]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4912204]]></link>
			<description><![CDATA[The concept of k-anonymity has received considerable attention due to the need of several organizations to release microdata without revealing the identity of individuals. Although all previous k-anonymity techniques assume the existence of a public database (PD) that can be used to breach privacy, none utilizes PD during the anonymization process. Specifically, existing generalization algorithms create anonymous tables using only the microdata table (MT) to be published, independently of the external knowledge available. This omission leads to high information loss. Motivated by this observation, we first introduce the concept of k-join-anonymity (KJA), which permits more effective generalization to reduce the information loss. Briefly, KJA anonymizes a superset of MT, which includes selected records from PD. We propose two methodologies for adapting k-anonymity algorithms to their KJA counterparts. The first generalizes the combination of MT and PD, under the constraint that each group should contain at least 1 tuple of MT (otherwise, the group is useless and discarded). The second anonymizes MT, and then, refines the resulting groups using PD. Finally, we evaluate the effectiveness of our contributions with an extensive experimental evaluation using real and synthetic data sets.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4912204]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>392</startPage>
			<endPage>403</endPage>
			<fileSize>3608</fileSize>
			<authors><![CDATA[Sacharidis, Dimitris;Mouratidis, Kyriakos;Papadias, Dimitris;]]></authors>
		</item>
		<item>
			<title><![CDATA[PAM: An Efficient and Privacy-Aware Monitoring Framework for Continuously Moving Objects]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4840343]]></link>
			<description><![CDATA[Efficiency and privacy are two fundamental issues in moving object monitoring. This paper proposes a privacy-aware monitoring (PAM) framework that addresses both issues. The framework distinguishes itself from the existing work by being the first to holistically address the issues of location updating in terms of monitoring accuracy, efficiency, and privacy, particularly, when and how mobile clients should send location updates to the server. Based on the notions of safe region and most probable result, PAM performs location updates only when they would likely alter the query results. Furthermore, by designing various client update strategies, the framework is flexible and able to optimize accuracy, privacy, or efficiency. We develop efficient query evaluation/reevaluation and safe region computation algorithms in the framework. The experimental results show that PAM substantially outperforms traditional schemes in terms of monitoring accuracy, CPU cost, and scalability while achieving close-to-optimal communication cost.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4840343]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>404</startPage>
			<endPage>419</endPage>
			<fileSize>1986</fileSize>
			<authors><![CDATA[Hu, Haibo;Xu, Jianliang;Lee, Dik Lun;]]></authors>
		</item>
		<item>
			<title><![CDATA[Ranked Query Processing in Uncertain Databases]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394984]]></link>
			<description><![CDATA[Recently, many new applications, such as sensor data monitoring and mobile device tracking, raise up the issue of uncertain data management. Compared to "certain&#x0201D; data, the data in the uncertain database are not exact points, which, instead, often reside within a region. In this paper, we study the ranked queries over uncertain data. In fact, ranked queries have been studied extensively in traditional database literature due to their popularity in many applications, such as decision making, recommendation raising, and data mining tasks. Many proposals have been made in order to improve the efficiency in answering ranked queries. However, the existing approaches are all based on the assumption that the underlying data are exact (or certain). Due to the intrinsic differences between uncertain and certain data, these methods are designed only for ranked queries in certain databases and cannot be applied to uncertain case directly. Motivated by this, we propose novel solutions to speed up the probabilistic ranked query (PRank) with monotonic preference functions over the uncertain database. Specifically, we introduce two effective pruning methods, spatial and probabilistic pruning, to help reduce the PRank search space. A special case of PRank with linear preference functions is also studied. Then, we seamlessly integrate these pruning heuristics into the PRank query procedure. Furthermore, we propose and tackle the PRank query processing over the join of two distinct uncertain databases. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches in answering PRank queries, in terms of both wall clock time and the number of candidates to be refined.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394984]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>420</startPage>
			<endPage>436</endPage>
			<fileSize>2937</fileSize>
			<authors><![CDATA[Lian, Xiang;Chen, Lei;]]></authors>
		</item>
		<item>
			<title><![CDATA[Spectral Anonymization of Data]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4840345]]></link>
			<description><![CDATA[The goal of data anonymization is to allow the release of scientifically useful data in a form that protects the privacy of its subjects. This requires more than simply removing personal identifiers from the data because an attacker can still use auxiliary information to infer sensitive individual information. Additional perturbation is necessary to prevent these inferences, and the challenge is to perturb the data in a way that preserves its analytic utility. No existing anonymization algorithm provides both perfect privacy protection and perfect analytic utility. We make the new observation that anonymization algorithms are not required to operate in the original vector-space basis of the data, and many algorithms can be improved by operating in a judiciously chosen alternate basis. A spectral basis derived from the data's eigenvectors is one that can provide substantial improvement. We introduce the term spectral anonymization to refer to an algorithm that uses a spectral basis for anonymization, and give two illustrative examples. We also propose new measures of privacy protection that are more general and more informative than existing measures, and a principled reference standard with which to define adequate privacy protection.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4840345]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>437</startPage>
			<endPage>446</endPage>
			<fileSize>1098</fileSize>
			<authors><![CDATA[Lasko, Thomas A.;Vinterbo, Staal A.;]]></authors>
		</item>
		<item>
			<title><![CDATA[ViDE: A Vision-Based Approach for Deep Web Data Extraction]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4840351]]></link>
			<description><![CDATA[Deep Web contents are accessed by queries submitted to Web databases and the returned data records are enwrapped in dynamically generated Web pages (they will be called deep Web pages in this paper). Extracting structured data from deep Web pages is a challenging problem due to the underlying intricate structures of such pages. Until now, a large number of techniques have been proposed to address this problem, but all of them have inherent limitations because they are Web-page-programming-language-dependent. As the popular two-dimensional media, the contents on Web pages are always displayed regularly for users to browse. This motivates us to seek a different way for deep Web data extraction to overcome the limitations of previous works by utilizing some interesting common visual features on the deep Web pages. In this paper, a novel vision-based approach that is Web-page-programming-language-independent is proposed. This approach primarily utilizes the visual features on the deep Web pages to implement deep Web data extraction, including data record extraction and data item extraction. We also propose a new evaluation measure revision to capture the amount of human effort needed to produce perfect extraction. Our experiments on a large set of Web databases show that the proposed vision-based approach is highly effective for deep Web data extraction.]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=4840351]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>447</startPage>
			<endPage>460</endPage>
			<fileSize>3295</fileSize>
			<authors><![CDATA[Liu, Wei;Meng, Xiaofeng;Meng, Weiyi;]]></authors>
		</item>
		<item>
			<title><![CDATA[Call for Papers: Cloud Data Management]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394985]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394985]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>461</startPage>
			<endPage>461</endPage>
			<fileSize>36</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[New IEEE Transactions on Affective Computing]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394986]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394986]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>462</startPage>
			<endPage>462</endPage>
			<fileSize>147</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[7 Reasons for Joining the IEEE Computer Society]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394987]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394987]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>463</startPage>
			<endPage>463</endPage>
			<fileSize>604</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[Raise Your Standards Software Development Certification]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394988]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394988]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>464</startPage>
			<endPage>464</endPage>
			<fileSize>327</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[Cover3]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394989]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394989]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>c3</startPage>
			<endPage>c3</endPage>
			<fileSize>204</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[Cover4]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394990]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[March  2010]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5394981&arnumber=5394990]]></guid>
			<volume>22</volume>
			<issue>3</issue>
			<startPage>c4</startPage>
			<endPage>c4</endPage>
			<fileSize>162</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
	</channel>
</rss>