Comparing Dissimilarity Measures for Symbolic Data Analysis
|
|
- Leonard McKinney
- 8 years ago
- Views:
Transcription
1 Comaring Dissimilarity Measures for Symbolic Data Analysis Donato MALERBA, Floriana ESPOSITO, Vincenzo GIOVIALE and Valentina TAMMA Diartimento di Informatica, University of Bari Via Orabona 4 76 Bari, Italy {malerba, esosito}@di.uniba.it, vincenzo_gio@yahoo.it, valli@csc.liv.ac.uk Abstract: Symbolic data analysis aims at extending classical data analysis techniues to manage symbolic data, which are a form of aggregated data. This extension asses through the definition of a new set of dissimilarity measures. Many of them have been reorted in the literature, but no comarative study has been erformed. This aer resents an emirical evaluation of dissimilarity measures roosed for a restricted class of symbolic data, namely Boolean symbolic obects. To define a ground truth for the emirical evaluation, a data set with a fully understandable and exlainable roerty has been selected. Emirical results show a variety of, sometimes unexected, behaviours of the comared measures. Keywords: Symbolic data analysis, Boolean symbolic obects, dissimilarity measure. 1. Introduction Most of statistical techniues for data analysis have been designed for a relatively simle situation: the unit for statistical analysis is an individual (e.g., a erson or an obect) described by a well defined set of random variables (either ualitative or uantitative), each of which result in ust one single value. Nowadays, data analysts are confronted with new challenges: they are asked to rocess data that go beyond the classical framework, as in the case of data concerning more or less homogeneous classes or grous of individuals (second-order obects) instead of single individuals (first-order obects). A tyical situation is that of census data, which raise rivacy issues in all governmental agencies that distribute them. To guarantee that data analysts cannot identify an individual or a single business establishment, data are made available in aggregate form. Data aggregations by census tracts or by enumeration districts are examles of second-order obects. Presently at the Deartment of Comuter Science, The University of Liverool, Chadwick Building, Peach St., Liverool L69 7ZF, UK. 473
2 Donato Malerba, Floriana Esosito, Vincenzo Gioviale and Valentina Tamma Aggregated data describe a grou of individuals by set-valued or modal variables. A variable Y defined for all elements k of a set E is termed set-valued with the domain Y if it takes its values in P(Y)={U U Y }, that is the ower set of Y. When Y(k) is finite for each k, than Y is called multi-valued. A single-valued variable is a secial case of set-valued variable for which Y(k) =1 for each k. When an order relation is defined on Y then the value returned by a set-valued variable can be exressed by an interval [,], and Y is termed an interval variable. More generally, a modal variable is a set-valued variable with a measure or a (freuency, robability or weight) distribution associated to Y(k). A class or grou of individuals described by a number of set-valued or modal variables is termed symbolic data. Symbolic data lead to more comlex data tables called symbolic data tables. The extension of classical data analysis techniues to such tables is termed symbolic data analysis [1]. Imlementing an integrated software environment for both the construction of symbolic data tables from records of individuals and the analysis of symbolic data has been the general aim of the three-years ESPRIT roect SODAS 1 (Symbolic Official Data Analysis System), concluded in November The recently started three-years IST roect ASSO (Analysis System of Symbolic Official Data) is intended to imrove the SODAS rototye with resect to several asects (management of symbolic data, new or imroved data analysis methods, new visualization techniues). An imortant module of the SODAS software is that concerning the comutation of some dissimilarity measures. Indeed, the extension of statistical techniues to symbolic data reuires the secification of some dissimilarity (or conversely, similarity) measures. Many formulations of dissimilarity measures for symbolic data have been reorted in literature [5], but no comarative study on their suitability to real-world roblems has been erformed. This aer resents an emirical evaluation of dissimilarity measures roosed for a restricted class of symbolic data, namely Boolean symbolic obects (BSOs). To define a ground truth for the emirical evaluation, a data set with a fully understandable and exlainable roerty has been selected. Emirical results show a variety of, sometimes unexected, behaviours of the comared measures. The reorted exerimentation is the first ste towards the fulfillment of one of the obectives of the ASSO roect, namely comaring various dissimilarity measures to suort users in selecting the best measure for their data analysis roblem. 2. Dissimilarity measures for Boolean Symbolic Obects Henceforth, the term dissimilarity measure d on a set of obects E refers to a real valued * function on E E such that: d a d( a, a) d( a, b) d( b, a) for all a,be. Conversely, a similarity measure s on a set of obects E is a real valued function on E E such that: * * * * * s a s( a, a) s( a, b) s( b, a) for all a,be. Generally, d a d and s a s for each obect a in E, and more secifically, d * = 1 while s * =. Studies on their roerties can be limited to dissimilarity measures alone, since it is always ossible to transform a similarity 1 The SODAS software is freely distributed by CISIA at the following URL address: htt:// 474
3 Comaring dissimilarity measures for symbolic data analysis measure into a dissimilarity one with the same roerties. Many methods have been reorted in the literature to derive dissimilarity measures from a matrix of observed data [4], or, more generally, for a set of symbolic obects [5]. In the following, only some measures roosed for BSOs are briefly reorted. Let a and b be two BSOs: a = [Y 1 =A 1 ] [Y 2 =A 2 ]... [Y =A ] b = [Y 1 =B 1 ] [Y 2 =B 2 ]... [Y =B ] where each variable Y takes values in a domain Y and A and B are subsets of Y. It is ossible to define a dissimilarity measure between two BSOs a and b by aggregating dissimilarity values comuted indeendently at the level of single variables Y (comonentwise dissimilarities). A classical aggregation function is the generalized Minkowski metric. However, another class of measures defined for BSOs is based on the the notion of descrition otential, (a), which is defined as the volume of the Cartesian roduct A 1 A 2... A. For this class of measures, no comonentwise decomosition is necessary, so that no function is reuired to aggregate dissimilarities comuted indeendently for each variable. The list of dissimilarity measures considered in this study are reorted in Table 1, where they are denoted as in the SODAS software, namely: U_1: Gowda and Diday s dissimilarity measure [6]; U_2: Ichino and Yaguchi s first formulation of a dissimilarity measure [7]; U_3: Ichino and Yaguchi s dissimilarity measure normalized w.r.t. domain length [7]; U_4: Ichino and Yaguchi s normalized and weighted dissimilarity measure [7]; SO_1: De Carvalho s dissimilarity measure [2]; SO_2: De Carvalho s extension of Ichino and Yaguchi s dissimilarity [2]; SO_3: De Carvalho s first dissimilarity measure based on descrition otential [3]; SO_4: De Carvalho s second dissimilarity measure based on descrition otential [3]; SO_5: De Carvalho s normalized dissimilarity measure based on descrition otential [3]; C_1: De Carvalho s normalized dissimilarity measure for constrained BSOs [3]. The term constrained BSO refers to the fact that some deendences between variables are defined, namely either hierarchical deendences (mother-daughter) which establish conditions for some variables being not measurable (not-alicable values), or logical deendences which establish the set of ossible values for a variable Y conditioned by the set of values taken by another variable Y k. An investigation of the effect of constraints on the comutation of dissimilarity measures is out of the scoe of this aer, nevertheless it is always ossible to aly the measures defined for constrained BSOs to unconstrained BSOs. This exlains why C_1 has been considered in the emirical comarison reorted in the next section. 3. Exerimental evaluation Many dissimilarity measures have been roosed for symbolic data analysis, nevertheless they have never been comared in order to understand both their common and their eculiar roerties. In this section, an emirical evaluation is reorted with reference to a data set for which a desirable behaviour of a dissimilarity measure can be defined. The data set is called Abalone data and is available at the UCI Machine Learning Reository 475
4 Donato Malerba, Floriana Esosito, Vincenzo Gioviale and Valentina Tamma Table 1. Dissimilarity measures available in the DI method for BSO s, and related arameters. Name Comonentwise Obectwise dissimilarity measure dissimilarity measure U_1 D () (A, B ) = D (A, B ) + () D s (A, B ) + D c (A, B ) d(a, b) = D (A, B) 1 where D (A, B ) is due to osition, D s (A, B ) is due to sanning and D c (A,B ) is due to content. U_2 (A, B ) = A B A B + (2A B d (a, b) = A B A, B ) 1 where meet () and oin () are two Cartesian oerators. U_3 ( A, B ) ( A, B ) Y d (a, b) = A, B U_4 ( A, B ) SO_1 d i (A,B ) i=1,...,5 SO_2 SO_3 none SO_4 none SO_5 none ' A, B ( A, B ) C_1 d i (A,B ) i=1,...,5 Y A, B A B 1 d (a, b) = A, B w 1 i d (a, b) = w di A, B 1 d' (a, b) = 1 ' A, 1 B d 1 (a, b) = (a b) (a b) + (2(a b) (a) (b)) ' ( a b ) ( a b ) ( 2 ( a b ) ( a ) ( b )) d2( a,b ) E ( a ) where (a E ) = [Y 1 Y 1 ] [Y Y ] ( a b ) ( a b ) ( ( a b ) ( a ) ( b )) d ' 2 3( a,b ) ( a b ) i 1 d (a, b) = w d i J 1 A, B ( ) where () is the indicator function, 476
5 Comaring dissimilarity measures for symbolic data analysis Table 2. Attributes of the Abalone data set Attribute Name Data Tye Unit Descrition Sex Nominal M, F. I (infant) Length Continuous mm Longest shell measurement Diameter Continuous mm Perendicular to length Height Continuous mm Measured with meat in shell Whole weight Continuous grams Weight of the whole abalone Shucked weight Continuous grams Weight of the meat Viscera weight Continuous grams Gut weight after bleeding Shell weight Continuous grams Weigh of the dried shell Rings Integer Number of rings (URL: htt:// It contains 4177 cases of marine crustaceans, which are described by means of the nine attributes listed in Table 2. There are no missing values in the data. Generally this data set is used for rediction tasks. The number of rings (last attribute) is the value to be redicted from which it is ossible to know the age in years of the crustacean by adding 1.5 to the number of rings. Since the deendent attribute is integervalued, this database has been extensively investigated in emirical studies concerning regression-tree induction [8,1]. The number of rings varies between 1 and 29, with samle mean eual to and samle standard deviation eual to (there are few cases of crustaceans with less than 3 or more than 25 rings). The erformance of regression-tree induction systems reorted in the literature is generally high, meaning that the eight indeendent attributes are actually sufficiently informative for the intended rediction task. In other words, two abalones with the same number of rings should also resent similar values for the attributes sex, length, diameter, height, and so on. Basing uon this consideration we exect to observe that the degree of dissimilarity between crustaceans comuted on the indeendent attributes do actually be roortional to the dissimilarity in the deendent attribute (i.e., difference in the number of rings). We will call this roerty as monotonic increasing dissimilarity (shortly, MID roerty). Abalone data can be aggregated into symbolic obects, each of which corresond to a range of values for the number of rings. In articular, nine BSOs have been generated by alying the DB2SO facility [9] available in the SODAS software (see Table 3). The dissimilarity measures briefly resented in Section 2 have been alied to the BSOs reviously illustrated. In this examle the value of the arameter is set to.5, the order of ower is 2 and the weights are uniformly distributed. Results are deicted in Figure 1. Dissimilarities are reorted along the vertical axis, while BSOs are listed along the horizontal axis, in ascending order with resect to the number of rings. Each line reresents the dissimilarity between a given BSO and the subseuent BSOs in the list. The number of lines in each grah is eight, since there are nine BSOs. For the sake of clarity, the lower triangular matrix of the dissimilarities deicted in the grah labeled Abalone- U_1 is reorted in Table
6 Donato Malerba, Floriana Esosito, Vincenzo Gioviale and Valentina Tamma Table 3. Boolean symbolic obects generated by artitioning the Rings attribute into nine intervals of eual length BSO Rings Sex Length Diameter Height Whole Shucked Viscera Shell I,M [.8:.24] [.5:.17] [.1:.6] [.:.7] [.:.3] [.:.1] [.:.2] I,M,F [.13:.66] [.9:.47] [.:.18] [.1:1.37] [.:.64] [.:.29] [.:.35] I,M,F [.2:.75] [.16:.58] [.:1.13] [.4:2.33] [.2:1.25] [.1:.54] [.2:.56] 4 1- I,M,F [.29:.78] [.22:.63] [.6:.51] [.:2.78] [.4:1.49] [.2:.76] [.4:.73] I,M,F [.32:.81] [.25:.65] [.8:.25] [.16:2.55] [.6:1.35] [.3:.57] [.5:.8] I,M,F [.4:.77] [.31:.6] [.1:.24] [.35:2.83] [.11:1.15] [.6:.48] [.:1.] I,M,F [.45:.74] [.35:.59] [.:.23] [.41:2.13] [.11:.87] [.7:.49] [.16:.85] M,F [.45:.8] [.38:.63] [.14:.22] [.64:2.53] [.16:.93] [.11:.59] [.24:.71] M,F [.55:.7] [.47:.58] [.18:.22] [1.6:2.18] [.32:.75] [.19:.39] [.38:.88] It is noteworthy that the MID roerty does not hold when the dissimilarity among BSOs is comuted by means of Gowda and Diday s measure (U_1). Surrisingly, old crustaceans with a high number of rings (25-29) are considered more similar to very young crustaceans with low number of rings (1-3) than to middle aged abalones with 1618 rings. Actually, for all numeric variables the dissimilarity comonents due to osition (D ) and content (D c ) increase along the horizontal axis, while the comonent due to sanning (D s ) first increases and then decreases. Sanning measures the difference between two interval widths and indeed BSO1 and BSO9 seem retty similar due to the fact that continuous variables have intervals with the same (small) width desite the fact that the intervals are uite distant. Incidentally, such intervals are relatively small since there are few cases of abalones aggregated into BSO1 and BSO9. Thus, U_1 can lead to unexected results in those cases in which BSOs are generated from uneually distributed cases with resect to a given class variable, such as rings. Also for the dissimilarity measure U_2 the MID roerty does not hold and the first BSO is the most atyical. In articular, BSO1 and BSO7 are more similar than BSO1 and BSO4. This can be exlained by noting that the dissimilarity comonent due to meet () has no effect when is set to.5, while the oin () of intervals in BSO1 and BSO7 is lower than the oin of intervals in BSO1 and BSO4, since all numerical intervals of BSO7 are included in the corresonding intervals for BSO4. Generalizing, we note that it is advisable not to nullify the effect of the meet oerator by setting =.5, otherwise anomalous similarities can be found. Similar considerations aly to the dissimilarity measures U_3 and U_4, although their normalization factor (i.e., domain Table 4. Dissimilarity values comuted by means of the dissimilarity U_1. Rings
7 Comaring dissimilarity measures for symbolic data analysis Abalone - U_1 2,5 2 1,5 1,5 Abalone - U_ ,5 1,2,9,6,3 Abalone - U_ ,2,15,1,5 Abalone - U_ ,4,3,2,1 Abalone - SO_ ,25,2,15,1,5 Abalone - SO_ ,5 1,2,9,6,3 Abalone - SO_ ,4,3,2,1 Abalone - SO_ ,2,9,6,3 Abalone - SO_ ,2,9,6,3 Abalone - C_ Figure 1. Grahs of ten dissimilarity measures for the abalone data. 479
8 Donato Malerba, Floriana Esosito, Vincenzo Gioviale and Valentina Tamma cardinality) tend to reduce the effect of the missing meet comonent. On the contrary, the normalization by the oin volume (A B ), roosed by De Carvalho in SO_2, totally removes the roblem even when =.5, and the exected MID roerty is satisfied. The MID roerty is generally valid for SO_1 as well, and the atyical symbolic obect is still BSO1. However, in this case, the numerical variables have almost no effect on the comutation of the dissimilarity, since the agreement index at the numerator of each d i, namely =(A B ), is very small. As a matter of fact, the dissimilarity between two symbolic obects is comuted on the basis of the only nominal variable sex. In the case of SO_3, the atyical obect is the third, while BSO1 is considered similar to all other symbolic obects, including BSO9. Once again, the contribution of the meet is nullified by setting =.5, nevertheless the situation is uite different from that resented in the case of U_2. The effect of the oin is multilicative in the comutation of the descrition otential defined by De Carvalho, while it is additive in Ichino and Yaguchi s dissimilarity measures. In SO_3 small intervals can zero the dissimilarity, while in U_2 they have ractically no effect. On the contrary, a single large interval can have a strong imact in U_2, while it may have no effect in SO_3. In the Abalone data set, small intervals are obtained by alying the oin oerator to continuous variables of symbolic obects BSO i, i3. This exlains the strange behaviour of lines deicted in the grah Abalone SO_3. Also in SO_5, which is a normalized version of SO_3, the MID roerty is not satisfied even though it has a more regular behaviour than SO_3 since the contribution of the oin oerator on numerical variables is reduced. Finally, in the case of C_1, the MID roerty is satisfied. According to this measure all symbolic obects are uite dissimilar (the maximum distance is 1.), and the most atyical is that reresenting young abalones (BSO1). Summarizing, the following conclusions can be drawn from this emirical evaluation: 1. Only three dissimilarity measures roosed by de Carvalho, namely SO_1, SO_2 and C_1, satisfy the MID roerty. For all these measures the less tyical obect is that reresenting very young abalones (BSO1). 2. When BSOs are generated from uneually distributed cases with resect to a given class variable, the actual size of variable value sets A i might simly reflect the original distribution of cases. In articular, small value sets may be due to the scantiness of cases used in the BSO generation rocess, while large value sets may occur because of the natural variability in a large oulation of cases used to synthesize BSOs. When this haens, distance measures based on the sanning factor (e.g., U_1) may lead to unexected results. 3. In Ichino and Yaguchi s measures (i.e., U_2, U_3 and U_4), the contribution of the meet oerator should not be nullified by setting the arameter to.5. An intermediate value between and.5 is generally recommended. Moreover, the normalization roosed by de Carvalho (see SO_2) show better results. 4. In the case of continuous variables, the width of value intervals is critical, since dissimilarities measures based on additive aggregation tend to return high values when only one comonentwise dissimilarity is uite large, while measures based on descrition otential return small values when only one comonentwise dissimilarity is uite small. 48
9 Comaring dissimilarity measures for symbolic data analysis 4. Conclusions In symbolic data analysis a key role is layed by the comutation of dissimilarity measures. Many measures have been roosed in the literature, although a comarison that investigates their alicability to real data has never been reorted. The main difficulty was due to the lack of a standard in the reresentation of symbolic obects and the necessity of imlementing many dissimilarity measures. The software roduced by the ESPRIT Proect SODAS has artially solved this roblem by defining a suite of modules that enable the generation, visualization and maniulation of symbolic obects. In this work, a comarative study of the dissimilarity measures for BSOs is reorted with reference to a articular data set for which an exected roerty could be defined. Interestingly enough, such a roerty has been observed only for some dissimilarity measures, which actually show very different behaviours. There are a number of ossible directions for future research. One is to exeriment whether other data sets with fully understandable and exlainable roerties related to the roximity concet. Another direction is to extend the emirical evaluation to dissimilarity measure defined on robabilistic symbolic obects. A third direction is to develo new dissimilarity measures for symbolic data that remove the two basic assumtions, namely variable indeendence and eual attribute relevance. Acknowledgements This work was suorted artly by the Euroean IST roect no (ASSO). References [1] Bock, H.H., Diday, E. (eds.): Analysis of Symbolic Data.Exloratory Methods for Extracting Statistical Information from Comlex Data, Series: Studies in Classification, Data Analysis, and Knowledge Organisation, Vol. 15, Sringer-Verlag, Berlin, (2), ISBN [2] de Carvalho, F.A.T.: Proximity coefficients between Boolean symbolic obects. In: Diday, E. et al. (eds.): New Aroaches in Classification and Data Analysis, Series: Studies in Classification, Data Analysis, and Knowledge Organisation, Vol. 5, Sringer-Verlag, Berlin, (1994) [3] de Carvalho, F.A.T.: Extension based roximity coefficients between constrained Boolean symbolic obects. In: Hayashi, C. et al. (eds.): Proc. of IFCS 96, Sringer, Berlin, (1998) [4] Esosito, F., Malerba, D., Tamma, V., and Bock, H.H.: Classical resemblance measures. In: Bock, H.H, Diday, E. (eds.): Analysis of Symbolic Data. Exloratory Methods for extracting Statistical Information from Comlex Data, Series: Studies in Classification, Data Analysis, and Knowledge Organisation, Vol. 15, Sringer-Verlag, Berlin, (2) [5] Esosito, F., Malerba, D., Tamma, V.: Dissimilarity Measures for Symbolic Obects. In: Bock, H.H., Diday, E. (eds.): Analysis of Symbolic Data. Exloratory Methods for extracting Statistical Information from Comlex Data, Series: Studies in Classification, Data Analysis, and Knowledge Organisation, Vol. 15, Sringer-Verlag, Berlin, (2) [6] Gowda, K. C., Diday, E.: Symbolic clustering using a new dissimilarity measure. In Pattern Recognition, Vol. 24, No. 6, (1991) [7] Ichino, M., Yaguchi, H.: Generalized Minkowski Metrics for Mixed Feature-Tye Data Analysis. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 24, No. 4, (1994) [8] Robnik-Šikona, M., Kononenko, I.: Pruning regression trees with MDL. In: Prade, H. (ed.): Proc. of the 13th Euroean Conference on Artificial Intelligence, John Wiley & Sons, Chichester, England, (1998) [9] Stéhan, V., Hébrail, G., and Lechevallier, Y.: Generation of Symbolic Obects from Relational Databases. In: Bock, H.H., Diday, E. (eds.): Analysis of Symbolic Data. Exloratory Methods for extracting Statistical Information from Comlex Data, Series: Studies in Classification, Data Analysis, and Knowledge Organisation, Vol. 15, Sringer-Verlag, Berlin, (2) [1] Torgo, L.: Functional Models for Regression Tree Leaves. In: Fisher, D. (ed.): Proc. of the International Machine Learning, Morgan Kaufmann, San Francisco, CA, (1997)
The impact of metadata implementation on webpage visibility in search engine results (Part II) q
Information Processing and Management 41 (2005) 691 715 www.elsevier.com/locate/inforoman The imact of metadata imlementation on webage visibility in search engine results (Part II) q Jin Zhang *, Alexandra
More informationThe risk of using the Q heterogeneity estimator for software engineering experiments
Dieste, O., Fernández, E., García-Martínez, R., Juristo, N. 11. The risk of using the Q heterogeneity estimator for software engineering exeriments. The risk of using the Q heterogeneity estimator for
More informationDAY-AHEAD ELECTRICITY PRICE FORECASTING BASED ON TIME SERIES MODELS: A COMPARISON
DAY-AHEAD ELECTRICITY PRICE FORECASTING BASED ON TIME SERIES MODELS: A COMPARISON Rosario Esínola, Javier Contreras, Francisco J. Nogales and Antonio J. Conejo E.T.S. de Ingenieros Industriales, Universidad
More informationAutomatic Search for Correlated Alarms
Automatic Search for Correlated Alarms Klaus-Dieter Tuchs, Peter Tondl, Markus Radimirsch, Klaus Jobmann Institut für Allgemeine Nachrichtentechnik, Universität Hannover Aelstraße 9a, 0167 Hanover, Germany
More informationEvaluating a Web-Based Information System for Managing Master of Science Summer Projects
Evaluating a Web-Based Information System for Managing Master of Science Summer Projects Till Rebenich University of Southamton tr08r@ecs.soton.ac.uk Andrew M. Gravell University of Southamton amg@ecs.soton.ac.uk
More informationConcurrent Program Synthesis Based on Supervisory Control
010 American Control Conference Marriott Waterfront, Baltimore, MD, USA June 30-July 0, 010 ThB07.5 Concurrent Program Synthesis Based on Suervisory Control Marian V. Iordache and Panos J. Antsaklis Abstract
More informationManaging specific risk in property portfolios
Managing secific risk in roerty ortfolios Andrew Baum, PhD University of Reading, UK Peter Struemell OPC, London, UK Contact author: Andrew Baum Deartment of Real Estate and Planning University of Reading
More informationProject Management and. Scheduling CHAPTER CONTENTS
6 Proect Management and Scheduling HAPTER ONTENTS 6.1 Introduction 6.2 Planning the Proect 6.3 Executing the Proect 6.7.1 Monitor 6.7.2 ontrol 6.7.3 losing 6.4 Proect Scheduling 6.5 ritical Path Method
More informationTime-Cost Trade-Offs in Resource-Constraint Project Scheduling Problems with Overlapping Modes
Time-Cost Trade-Offs in Resource-Constraint Proect Scheduling Problems with Overlaing Modes François Berthaut Robert Pellerin Nathalie Perrier Adnène Hai February 2011 CIRRELT-2011-10 Bureaux de Montréal
More informationLarge-Scale IP Traceback in High-Speed Internet: Practical Techniques and Theoretical Foundation
Large-Scale IP Traceback in High-Seed Internet: Practical Techniques and Theoretical Foundation Jun Li Minho Sung Jun (Jim) Xu College of Comuting Georgia Institute of Technology {junli,mhsung,jx}@cc.gatech.edu
More informationA MOST PROBABLE POINT-BASED METHOD FOR RELIABILITY ANALYSIS, SENSITIVITY ANALYSIS AND DESIGN OPTIMIZATION
9 th ASCE Secialty Conference on Probabilistic Mechanics and Structural Reliability PMC2004 Abstract A MOST PROBABLE POINT-BASED METHOD FOR RELIABILITY ANALYSIS, SENSITIVITY ANALYSIS AND DESIGN OPTIMIZATION
More informationFailure Behavior Analysis for Reliable Distributed Embedded Systems
Failure Behavior Analysis for Reliable Distributed Embedded Systems Mario Tra, Bernd Schürmann, Torsten Tetteroo {tra schuerma tetteroo}@informatik.uni-kl.de Deartment of Comuter Science, University of
More informationLearning Human Behavior from Analyzing Activities in Virtual Environments
Learning Human Behavior from Analyzing Activities in Virtual Environments C. BAUCKHAGE 1, B. GORMAN 2, C. THURAU 3 & M. HUMPHRYS 2 1) Deutsche Telekom Laboratories, Berlin, Germany 2) Dublin City University,
More informationService Network Design with Asset Management: Formulations and Comparative Analyzes
Service Network Design with Asset Management: Formulations and Comarative Analyzes Jardar Andersen Teodor Gabriel Crainic Marielle Christiansen October 2007 CIRRELT-2007-40 Service Network Design with
More informationTwo-resource stochastic capacity planning employing a Bayesian methodology
Journal of the Oerational Research Society (23) 54, 1198 128 r 23 Oerational Research Society Ltd. All rights reserved. 16-5682/3 $25. www.algrave-journals.com/jors Two-resource stochastic caacity lanning
More informationFinding a Needle in a Haystack: Pinpointing Significant BGP Routing Changes in an IP Network
Finding a Needle in a Haystack: Pinointing Significant BGP Routing Changes in an IP Network Jian Wu, Zhuoqing Morley Mao University of Michigan Jennifer Rexford Princeton University Jia Wang AT&T Labs
More informationStatic and Dynamic Properties of Small-world Connection Topologies Based on Transit-stub Networks
Static and Dynamic Proerties of Small-world Connection Toologies Based on Transit-stub Networks Carlos Aguirre Fernando Corbacho Ramón Huerta Comuter Engineering Deartment, Universidad Autónoma de Madrid,
More informationAn inventory control system for spare parts at a refinery: An empirical comparison of different reorder point methods
An inventory control system for sare arts at a refinery: An emirical comarison of different reorder oint methods Eric Porras a*, Rommert Dekker b a Instituto Tecnológico y de Estudios Sueriores de Monterrey,
More informationFrom Simulation to Experiment: A Case Study on Multiprocessor Task Scheduling
From to Exeriment: A Case Study on Multirocessor Task Scheduling Sascha Hunold CNRS / LIG Laboratory Grenoble, France sascha.hunold@imag.fr Henri Casanova Det. of Information and Comuter Sciences University
More informationCOST CALCULATION IN COMPLEX TRANSPORT SYSTEMS
OST ALULATION IN OMLEX TRANSORT SYSTEMS Zoltán BOKOR 1 Introduction Determining the real oeration and service costs is essential if transort systems are to be lanned and controlled effectively. ost information
More informationWeb Application Scalability: A Model-Based Approach
Coyright 24, Software Engineering Research and Performance Engineering Services. All rights reserved. Web Alication Scalability: A Model-Based Aroach Lloyd G. Williams, Ph.D. Software Engineering Research
More informationDesign of A Knowledge Based Trouble Call System with Colored Petri Net Models
2005 IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific Dalian, China Design of A Knowledge Based Trouble Call System with Colored Petri Net Models Hui-Jen Chuang, Chia-Hung
More informationOn the predictive content of the PPI on CPI inflation: the case of Mexico
On the redictive content of the PPI on inflation: the case of Mexico José Sidaoui, Carlos Caistrán, Daniel Chiquiar and Manuel Ramos-Francia 1 1. Introduction It would be natural to exect that shocks to
More informationAn Efficient Method for Improving Backfill Job Scheduling Algorithm in Cluster Computing Systems
The International ournal of Soft Comuting and Software Engineering [SCSE], Vol., No., Secial Issue: The Proceeding of International Conference on Soft Comuting and Software Engineering 0 [SCSE ], San Francisco
More informationMultiperiod Portfolio Optimization with General Transaction Costs
Multieriod Portfolio Otimization with General Transaction Costs Victor DeMiguel Deartment of Management Science and Oerations, London Business School, London NW1 4SA, UK, avmiguel@london.edu Xiaoling Mei
More informationLocal Connectivity Tests to Identify Wormholes in Wireless Networks
Local Connectivity Tests to Identify Wormholes in Wireless Networks Xiaomeng Ban Comuter Science Stony Brook University xban@cs.sunysb.edu Rik Sarkar Comuter Science Freie Universität Berlin sarkar@inf.fu-berlin.de
More informationX How to Schedule a Cascade in an Arbitrary Graph
X How to Schedule a Cascade in an Arbitrary Grah Flavio Chierichetti, Cornell University Jon Kleinberg, Cornell University Alessandro Panconesi, Saienza University When individuals in a social network
More informationENFORCING SAFETY PROPERTIES IN WEB APPLICATIONS USING PETRI NETS
ENFORCING SAFETY PROPERTIES IN WEB APPLICATIONS USING PETRI NETS Liviu Grigore Comuter Science Deartment University of Illinois at Chicago Chicago, IL, 60607 lgrigore@cs.uic.edu Ugo Buy Comuter Science
More informationRe-Dispatch Approach for Congestion Relief in Deregulated Power Systems
Re-Disatch Aroach for Congestion Relief in Deregulated ower Systems Ch. Naga Raja Kumari #1, M. Anitha 2 #1, 2 Assistant rofessor, Det. of Electrical Engineering RVR & JC College of Engineering, Guntur-522019,
More informationThe fast Fourier transform method for the valuation of European style options in-the-money (ITM), at-the-money (ATM) and out-of-the-money (OTM)
Comutational and Alied Mathematics Journal 15; 1(1: 1-6 Published online January, 15 (htt://www.aascit.org/ournal/cam he fast Fourier transform method for the valuation of Euroean style otions in-the-money
More informationThe Online Freeze-tag Problem
The Online Freeze-tag Problem Mikael Hammar, Bengt J. Nilsson, and Mia Persson Atus Technologies AB, IDEON, SE-3 70 Lund, Sweden mikael.hammar@atus.com School of Technology and Society, Malmö University,
More informationTitle: Stochastic models of resource allocation for services
Title: Stochastic models of resource allocation for services Author: Ralh Badinelli,Professor, Virginia Tech, Deartment of BIT (235), Virginia Tech, Blacksburg VA 2461, USA, ralhb@vt.edu Phone : (54) 231-7688,
More informationMachine Learning with Operational Costs
Journal of Machine Learning Research 14 (2013) 1989-2028 Submitted 12/11; Revised 8/12; Published 7/13 Machine Learning with Oerational Costs Theja Tulabandhula Deartment of Electrical Engineering and
More informationF inding the optimal, or value-maximizing, capital
Estimating Risk-Adjusted Costs of Financial Distress by Heitor Almeida, University of Illinois at Urbana-Chamaign, and Thomas Philion, New York University 1 F inding the otimal, or value-maximizing, caital
More informationProbabilistic models for mechanical properties of prestressing strands
Probabilistic models for mechanical roerties of restressing strands Luciano Jacinto a, Manuel Pia b, Luís Neves c, Luís Oliveira Santos b a Instituto Suerior de Engenharia de Lisboa, Rua Conselheiro Emídio
More informationOn the (in)effectiveness of Probabilistic Marking for IP Traceback under DDoS Attacks
On the (in)effectiveness of Probabilistic Maring for IP Tracebac under DDoS Attacs Vamsi Paruchuri, Aran Durresi 2, and Ra Jain 3 University of Central Aransas, 2 Louisiana State University, 3 Washington
More informationBeyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis
Psychological Methods 004, Vol. 9, No., 164 18 Coyright 004 by the American Psychological Association 108-989X/04/$1.00 DOI: 10.1037/108-989X.9..164 Beyond the F Test: Effect Size Confidence Intervals
More informationA Multivariate Statistical Analysis of Stock Trends. Abstract
A Multivariate Statistical Analysis of Stock Trends Aril Kerby Alma College Alma, MI James Lawrence Miami University Oxford, OH Abstract Is there a method to redict the stock market? What factors determine
More informationMigration to Object Oriented Platforms: A State Transformation Approach
Migration to Object Oriented Platforms: A State Transformation Aroach Ying Zou, Kostas Kontogiannis Det. of Electrical & Comuter Engineering University of Waterloo Waterloo, ON, N2L 3G1, Canada {yzou,
More informationEffect Sizes Based on Means
CHAPTER 4 Effect Sizes Based on Means Introduction Raw (unstardized) mean difference D Stardized mean difference, d g Resonse ratios INTRODUCTION When the studies reort means stard deviations, the referred
More informationRisk in Revenue Management and Dynamic Pricing
OPERATIONS RESEARCH Vol. 56, No. 2, March Aril 2008,. 326 343 issn 0030-364X eissn 1526-5463 08 5602 0326 informs doi 10.1287/ore.1070.0438 2008 INFORMS Risk in Revenue Management and Dynamic Pricing Yuri
More informationBuffer Capacity Allocation: A method to QoS support on MPLS networks**
Buffer Caacity Allocation: A method to QoS suort on MPLS networks** M. K. Huerta * J. J. Padilla X. Hesselbach ϒ R. Fabregat O. Ravelo Abstract This aer describes an otimized model to suort QoS by mean
More informationImplementation of Statistic Process Control in a Painting Sector of a Automotive Manufacturer
4 th International Conference on Industrial Engineering and Industrial Management IV Congreso de Ingeniería de Organización Donostia- an ebastián, etember 8 th - th Imlementation of tatistic Process Control
More informationAn important observation in supply chain management, known as the bullwhip effect,
Quantifying the Bullwhi Effect in a Simle Suly Chain: The Imact of Forecasting, Lead Times, and Information Frank Chen Zvi Drezner Jennifer K. Ryan David Simchi-Levi Decision Sciences Deartment, National
More informationRisk and Return. Sample chapter. e r t u i o p a s d f CHAPTER CONTENTS LEARNING OBJECTIVES. Chapter 7
Chater 7 Risk and Return LEARNING OBJECTIVES After studying this chater you should be able to: e r t u i o a s d f understand how return and risk are defined and measured understand the concet of risk
More informationLoad Balancing Mechanism in Agent-based Grid
Communications on Advanced Comutational Science with Alications 2016 No. 1 (2016) 57-62 Available online at www.isacs.com/cacsa Volume 2016, Issue 1, Year 2016 Article ID cacsa-00042, 6 Pages doi:10.5899/2016/cacsa-00042
More informationThe Economics of the Cloud: Price Competition and Congestion
Submitted to Oerations Research manuscrit The Economics of the Cloud: Price Cometition and Congestion Jonatha Anselmi Basque Center for Alied Mathematics, jonatha.anselmi@gmail.com Danilo Ardagna Di. di
More informationAn Associative Memory Readout in ESN for Neural Action Potential Detection
g An Associative Memory Readout in ESN for Neural Action Potential Detection Nicolas J. Dedual, Mustafa C. Ozturk, Justin C. Sanchez and José C. Princie Abstract This aer describes how Echo State Networks
More informationA Modified Measure of Covert Network Performance
A Modified Measure of Covert Network Performance LYNNE L DOTY Marist College Deartment of Mathematics Poughkeesie, NY UNITED STATES lynnedoty@maristedu Abstract: In a covert network the need for secrecy
More informationMonitoring Frequency of Change By Li Qin
Monitoring Frequency of Change By Li Qin Abstract Control charts are widely used in rocess monitoring roblems. This aer gives a brief review of control charts for monitoring a roortion and some initial
More informationLarge firms and heterogeneity: the structure of trade and industry under oligopoly
Large firms and heterogeneity: the structure of trade and industry under oligooly Eddy Bekkers University of Linz Joseh Francois University of Linz & CEPR (London) ABSTRACT: We develo a model of trade
More informationExpert Systems with Applications
Exert Systems with Alications 38 (2011) 11984 11997 Contents lists available at ScienceDirect Exert Systems with Alications journal homeage: www.elsevier.com/locate/eswa Review On the alication of genetic
More informationJena Research Papers in Business and Economics
Jena Research Paers in Business and Economics A newsvendor model with service and loss constraints Werner Jammernegg und Peter Kischka 21/2008 Jenaer Schriften zur Wirtschaftswissenschaft Working and Discussion
More informationPoint Location. Preprocess a planar, polygonal subdivision for point location queries. p = (18, 11)
Point Location Prerocess a lanar, olygonal subdivision for oint location ueries. = (18, 11) Inut is a subdivision S of comlexity n, say, number of edges. uild a data structure on S so that for a uery oint
More informationCRITICAL AVIATION INFRASTRUCTURES VULNERABILITY ASSESSMENT TO TERRORIST THREATS
Review of the Air Force Academy No (23) 203 CRITICAL AVIATION INFRASTRUCTURES VULNERABILITY ASSESSMENT TO TERRORIST THREATS Cătălin CIOACĂ Henri Coandă Air Force Academy, Braşov, Romania Abstract: The
More informationSoftware Cognitive Complexity Measure Based on Scope of Variables
Software Cognitive Comlexity Measure Based on Scoe of Variables Kwangmyong Rim and Yonghua Choe Faculty of Mathematics, Kim Il Sung University, D.P.R.K mathchoeyh@yahoo.com Abstract In this aer, we define
More informationMethods for Estimating Kidney Disease Stage Transition Probabilities Using Electronic Medical Records
(Generating Evidence & Methods to imrove atient outcomes) Volume 1 Issue 3 Methods for CER, PCOR, and QI Using EHR Data in a Learning Health System Article 6 12-1-2013 Methods for Estimating Kidney Disease
More informationTOWARDS REAL-TIME METADATA FOR SENSOR-BASED NETWORKS AND GEOGRAPHIC DATABASES
TOWARDS REAL-TIME METADATA FOR SENSOR-BASED NETWORKS AND GEOGRAPHIC DATABASES C. Gutiérrez, S. Servigne, R. Laurini LIRIS, INSA Lyon, Bât. Blaise Pascal, 20 av. Albert Einstein 69621 Villeurbanne, France
More informationService Network Design with Asset Management: Formulations and Comparative Analyzes
Service Network Design with Asset Management: Formulations and Comarative Analyzes Jardar Andersen Teodor Gabriel Crainic Marielle Christiansen October 2007 CIRRELT-2007-40 Service Network Design with
More informationInteraction Expressions A Powerful Formalism for Describing Inter-Workflow Dependencies
Interaction Exressions A Powerful Formalism for Describing Inter-Workflow Deendencies Christian Heinlein, Peter Dadam Det. Databases and Information Systems University of Ulm, Germany {heinlein,dadam}@informatik.uni-ulm.de
More informationSang Hoo Bae Department of Economics Clark University 950 Main Street Worcester, MA 01610-1477 508.793.7101 sbae@clarku.edu
Outsourcing with Quality Cometition: Insights from a Three Stage Game Theoretic Model Sang Hoo ae Deartment of Economics Clark University 950 Main Street Worcester, M 01610-1477 508.793.7101 sbae@clarku.edu
More informationANALYSING THE OVERHEAD IN MOBILE AD-HOC NETWORK WITH A HIERARCHICAL ROUTING STRUCTURE
AALYSIG THE OVERHEAD I MOBILE AD-HOC ETWORK WITH A HIERARCHICAL ROUTIG STRUCTURE Johann Lóez, José M. Barceló, Jorge García-Vidal Technical University of Catalonia (UPC), C/Jordi Girona 1-3, 08034 Barcelona,
More informationA Simple Model of Pricing, Markups and Market. Power Under Demand Fluctuations
A Simle Model of Pricing, Markus and Market Power Under Demand Fluctuations Stanley S. Reynolds Deartment of Economics; University of Arizona; Tucson, AZ 85721 Bart J. Wilson Economic Science Laboratory;
More informationAn Empirical Analysis of the Effect of Credit Rating on Trade Credit
011 International Conference on Financial Management and Economics IPED vol.11 (011) (011) ICSIT Press, Singaore n Emirical nalysis of the Effect of Credit ating on Trade Credit Jian-Hsin Chou¹ Mei-Ching
More informationImproved Algorithms for Data Visualization in Forensic DNA Analysis
Imroved Algorithms for Data Visualization in Forensic DNA Analysis Noor Maizura Mohamad Noor, Senior Member IACSIT, Mohd Iqbal akim arun, and Ahmad Faiz Ghazali Abstract DNA rofiles from forensic evidence
More informationSoftmax Model as Generalization upon Logistic Discrimination Suffers from Overfitting
Journal of Data Science 12(2014),563-574 Softmax Model as Generalization uon Logistic Discrimination Suffers from Overfitting F. Mohammadi Basatini 1 and Rahim Chiniardaz 2 1 Deartment of Statistics, Shoushtar
More informationBranch-and-Price for Service Network Design with Asset Management Constraints
Branch-and-Price for Servicee Network Design with Asset Management Constraints Jardar Andersen Roar Grønhaug Mariellee Christiansen Teodor Gabriel Crainic December 2007 CIRRELT-2007-55 Branch-and-Price
More informationPartial-Order Planning Algorithms todomainfeatures. Information Sciences Institute University ofwaterloo
Relating the Performance of Partial-Order Planning Algorithms todomainfeatures Craig A. Knoblock Qiang Yang Information Sciences Institute University ofwaterloo University of Southern California Comuter
More informationA Novel Architecture Style: Diffused Cloud for Virtual Computing Lab
A Novel Architecture Style: Diffused Cloud for Virtual Comuting Lab Deven N. Shah Professor Terna College of Engg. & Technology Nerul, Mumbai Suhada Bhingarar Assistant Professor MIT College of Engg. Paud
More informationFactoring Variations in Natural Images with Deep Gaussian Mixture Models
Factoring Variations in Natural Images with Dee Gaussian Mixture Models Aäron van den Oord, Benjamin Schrauwen Electronics and Information Systems deartment (ELIS), Ghent University {aaron.vandenoord,
More informationForensic Science International
Forensic Science International 214 (2012) 33 43 Contents lists available at ScienceDirect Forensic Science International jou r nal h o me age: w ww.els evier.co m/lo c ate/fo r sc iin t A robust detection
More informationModeling and Simulation of an Incremental Encoder Used in Electrical Drives
10 th International Symosium of Hungarian Researchers on Comutational Intelligence and Informatics Modeling and Simulation of an Incremental Encoder Used in Electrical Drives János Jób Incze, Csaba Szabó,
More informationAn actuarial approach to pricing Mortgage Insurance considering simultaneously mortgage default and prepayment
An actuarial aroach to ricing Mortgage Insurance considering simultaneously mortgage default and reayment Jesús Alan Elizondo Flores Comisión Nacional Bancaria y de Valores aelizondo@cnbv.gob.mx Valeria
More informationPrecalculus Prerequisites a.k.a. Chapter 0. August 16, 2013
Precalculus Prerequisites a.k.a. Chater 0 by Carl Stitz, Ph.D. Lakeland Community College Jeff Zeager, Ph.D. Lorain County Community College August 6, 0 Table of Contents 0 Prerequisites 0. Basic Set
More informationParticipation in Farm Markets in Rural Northwest Pakistan: A Regression Analysis
Pak. J. Commer. Soc. Sci. 2012 Vol. 6 (2), 348-356 Particiation in Farm Markets in Rural Northwest Pakistan: A Regression Analysis Inayatullah Jan Assistant Professor of Rural Develoment, Institute of
More informationProvable Ownership of File in De-duplication Cloud Storage
1 Provable Ownershi of File in De-dulication Cloud Storage Chao Yang, Jian Ren and Jianfeng Ma School of CS, Xidian University Xi an, Shaanxi, 710071. Email: {chaoyang, jfma}@mail.xidian.edu.cn Deartment
More informationMoving Objects Tracking in Video by Graph Cuts and Parameter Motion Model
International Journal of Comuter Alications (0975 8887) Moving Objects Tracking in Video by Grah Cuts and Parameter Motion Model Khalid Housni, Driss Mammass IRF SIC laboratory, Faculty of sciences Agadir
More informationElectronic Commerce Research and Applications
Electronic Commerce Research and Alications 12 (2013) 246 259 Contents lists available at SciVerse ScienceDirect Electronic Commerce Research and Alications journal homeage: www.elsevier.com/locate/ecra
More informationOn-the-Job Search, Work Effort and Hyperbolic Discounting
On-the-Job Search, Work Effort and Hyerbolic Discounting Thomas van Huizen March 2010 - Preliminary draft - ABSTRACT This aer assesses theoretically and examines emirically the effects of time references
More informationNew Approaches to Idea Generation and Consumer Input in the Product Development
New roaches to Idea Generation and Consumer Inut in the Product Develoment Process y Olivier Toubia Ingénieur, Ecole Centrale Paris, 000 M.S. Oerations Research, Massachusetts Institute of Technology,
More informationSTATISTICAL CHARACTERIZATION OF THE RAILROAD SATELLITE CHANNEL AT KU-BAND
STATISTICAL CHARACTERIZATION OF THE RAILROAD SATELLITE CHANNEL AT KU-BAND Giorgio Sciascia *, Sandro Scalise *, Harald Ernst * and Rodolfo Mura + * DLR (German Aerosace Centre) Institute for Communications
More informationOn Software Piracy when Piracy is Costly
Deartment of Economics Working aer No. 0309 htt://nt.fas.nus.edu.sg/ecs/ub/w/w0309.df n Software iracy when iracy is Costly Sougata oddar August 003 Abstract: The ervasiveness of the illegal coying of
More informationRed vs. Blue - Aneue of TCP congestion Control Model
In IEEE INFOCOM 2 A Study of Active Queue Management for Congestion Control Victor Firoiu Marty Borden 1 vfiroiu@nortelnetworks.com mborden@tollbridgetech.com Nortel Networks TollBridge Technologies 6
More informationThe Advantage of Timely Intervention
Journal of Exerimental Psychology: Learning, Memory, and Cognition 2004, Vol. 30, No. 4, 856 876 Coyright 2004 by the American Psychological Association 0278-7393/04/$12.00 DOI: 10.1037/0278-7393.30.4.856
More informationCharacterizing and Modeling Network Traffic Variability
Characterizing and Modeling etwork Traffic Variability Sarat Pothuri, David W. Petr, Sohel Khan Information and Telecommunication Technology Center Electrical Engineering and Comuter Science Deartment,
More informationStability Improvements of Robot Control by Periodic Variation of the Gain Parameters
Proceedings of the th World Congress in Mechanism and Machine Science ril ~4, 4, ianin, China China Machinery Press, edited by ian Huang. 86-8 Stability Imrovements of Robot Control by Periodic Variation
More informationTHE HEBREW UNIVERSITY OF JERUSALEM
האוניברסיטה העברית בירושלים THE HEBREW UNIVERSITY OF JERUSALEM FIRM SPECIFIC AND MACRO HERDING BY PROFESSIONAL AND AMATEUR INVESTORS AND THEIR EFFECTS ON MARKET By ITZHAK VENEZIA, AMRUT NASHIKKAR, and
More informationAn effective multi-objective approach to prioritisation of sewer pipe inspection
An effective multi-objective aroach to rioritisation of sewer ie insection L. Berardi 1 *, O.Giustolisi 1, D.A. Savic 2 and Z. Kaelan 2 1 Technical University of Bari, Civil and Environmental Engineering
More informationThe Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling
The Fundamental Incomatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsamling Michael Betancourt Deartment of Statistics, University of Warwick, Coventry, UK CV4 7A BETANAPHA@GMAI.COM Abstract
More informationA Virtual Machine Dynamic Migration Scheduling Model Based on MBFD Algorithm
International Journal of Comuter Theory and Engineering, Vol. 7, No. 4, August 2015 A Virtual Machine Dynamic Migration Scheduling Model Based on MBFD Algorithm Xin Lu and Zhuanzhuan Zhang Abstract This
More informationSimulink Implementation of a CDMA Smart Antenna System
Simulink Imlementation of a CDMA Smart Antenna System MOSTAFA HEFNAWI Deartment of Electrical and Comuter Engineering Royal Military College of Canada Kingston, Ontario, K7K 7B4 CANADA Abstract: - The
More informationSOFTWARE DEBUGGING WITH DYNAMIC INSTRUMENTATION AND TEST BASED KNOWLEDGE. A Thesis Submitted to the Faculty. Purdue University.
SOFTWARE DEBUGGING WITH DYNAMIC INSTRUMENTATION AND TEST BASED KNOWLEDGE A Thesis Submitted to the Faculty of Purdue University by Hsin Pan In Partial Fulfillment of the Requirements for the Degree of
More informationA Third Generation Automated Teller Machine Using Universal Subscriber Module with Iris Recognition
A Third Generation Automated Teller Machine Using Universal Subscriber Module with Iris Recognition B.Sundar Raj Assistant rofessor, Det of CSE, Bharath University, Chennai, TN, India. ABSTRACT: This aer
More informationPredicate Encryption Supporting Disjunctions, Polynomial Equations, and Inner Products
Predicate Encrytion Suorting Disjunctions, Polynomial Equations, and Inner Products Jonathan Katz Amit Sahai Brent Waters Abstract Predicate encrytion is a new aradigm for ublic-key encrytion that generalizes
More informationSynopsys RURAL ELECTRICATION PLANNING SOFTWARE (LAPER) Rainer Fronius Marc Gratton Electricité de France Research and Development FRANCE
RURAL ELECTRICATION PLANNING SOFTWARE (LAPER) Rainer Fronius Marc Gratton Electricité de France Research and Develoment FRANCE Synosys There is no doubt left about the benefit of electrication and subsequently
More informationA Certification Authority for Elliptic Curve X.509v3 Certificates
A Certification Authority for Ellitic Curve X509v3 Certificates Maria-Dolores Cano, Ruben Toledo-Valera, Fernando Cerdan Det of Information Technologies & Communications Technical University of Cartagena
More informationInt. J. Advanced Networking and Applications Volume: 6 Issue: 4 Pages: 2386-2392 (2015) ISSN: 0975-0290
2386 Survey: Biological Insired Comuting in the Network Security V Venkata Ramana Associate Professor, Deartment of CSE, CBIT, Proddatur, Y.S.R (dist), A.P-516360 Email: ramanacsecbit@gmail.com Y.Subba
More informationStochastic Derivation of an Integral Equation for Probability Generating Functions
Journal of Informatics and Mathematical Sciences Volume 5 (2013), Number 3,. 157 163 RGN Publications htt://www.rgnublications.com Stochastic Derivation of an Integral Equation for Probability Generating
More informationINFERRING APP DEMAND FROM PUBLICLY AVAILABLE DATA 1
RESEARCH NOTE INFERRING APP DEMAND FROM PUBLICLY AVAILABLE DATA 1 Rajiv Garg McCombs School of Business, The University of Texas at Austin, Austin, TX 78712 U.S.A. {Rajiv.Garg@mccombs.utexas.edu} Rahul
More informationHigh Quality Offset Printing An Evolutionary Approach
High Quality Offset Printing An Evolutionary Aroach Ralf Joost Institute of Alied Microelectronics and omuter Engineering University of Rostock Rostock, 18051, Germany +49 381 498 7272 ralf.joost@uni-rostock.de
More information