Statistical Intrusion Detector with Instance-Based Learning
|
|
- Moses Tucker
- 8 years ago
- Views:
Transcription
1 Iformatca 5 (00) xxx yyy Statstcal Itruso Detector wth Istace-Based Learg Iva Verdo, Boja Nova Faulteta za eletroteho raualštvo Uverza v Marboru Smetaova 7, 000 Marbor, Sloveja va.verdo@sol.et eywords: truso detecto, stace-based learg, reducto techques Receved: [Eter date] I ths paper we are dealg wth computer securty ssues. I ths very broad area, we focused o truso detecto, specfcally, o statstcal detecto. Our statstcal truso detector, preseted the paper, s based o Istace-based Learg wth the -earest Neghbours method. Statstcal detector requres a good ad small database of regular data to be able to valdate the actual traffc correctly ad promptly. Therefore we cosdered reducto techques of gathered data, based o clusterg. We adjusted the -earest Neghbours algorthm by comparg a sequece of actual data wth sequeces of regular data stead of comparg oly oe actual stace wth -earest regular staces.for ths purpose we explored four smlarty measure fuctos. Fally, our securty soluto VAL (Varost ALarm), cosstg of our statstcal detector, a SNORT rule-based truso detecto system, a ptables Lux frewall ad a maagemet cosole, s preseted. Itroducto I addto to the great opportutes ad beeft for mad, the emergece of global etworg the prevous decade has brought also serous securty threats to ts users. Itruso detecto ca be regarded as a tool that ca mprove the securty of local etwor ad/or dvdual hosts. Itruso Detecto Systems (IDS) ca prevet uauthorzed access to system resources ad data ad catch the attacer at the act. There are two ma approaches to truso detecto [6]. These are: rule-based msuse detecto ad statstcal based aomaly detecto. Each of them has ts strog ad wea pots. Rule-based detectors are better for teral securty (by that we mea securty sde the compay traet). O the other had, the strogest pot of statstcal detectors s the detecto of ovel, prevously uow ds of attacs whle they are wea at teral securty. Therefore t s reasoable to combe a rule-based ad a statstcal detector to a hybrd detector. The latest tred s to bloc the attacer IP address wth a frewall from the truso detector. Such systems are called truso preveto systems. Our securty soluto VAL ecompasses all these features. Istace-based Learg Istace-Based Learg (IBL) algorthms cosst of smply storg the preseted trag examples as well as ther attrbute lsts ad ther outcome (database of regular data). Ad whe a ew stace s ecoutered, a set of smlar, related staces s retreved from the memory ad used to classfy the actual (ew) stace accordg to the outcome of the majorty of related trag staces []. Ths d of classfcato s called target fucto. The outcome s our case ether 0 - ormal actvty or - truso. The followg are the most commo IBL target fuctos: -Nearest Neghbor Locally Weghted Regresso Radal Bass Fucto
2 Ttle of the paper Iformatca 3 (999) xxx yyy IBL approaches ca costruct a dfferet approxmato of the target fucto for each dstct ew stace to be classfed. Some techques oly costruct a local approxmato of the target fucto that apples the eghborhood of the ew query stace ad ever costruct a approxmato desged to perform well over the etre stace space. Ths s a advatage whe the target fucto s very complex, but ca stll be descrbed by a collecto of less complex local approxmatos [].. -Nearest Neghbour The -Nearest Neghbor algorthm s the most basc of all Istace-Based Learg (IBL) methods. The algorthm assumes all staces correspod to pots the - dmesoal space R. The earest eghbors of a stace are defed terms of stadard Eucldea geometry (dstaces betwee pots -dmesoal space). More precsely, let a arbtrary stace x be descrbed by the feature attrbute lst: < a (x), a (x), a 3 (x),..., a (x)>, where a r (x) deotes the value of the r th attrbute of stace x. I our case attrbute lst of the staces cossts of TCP pacet header parameters. The most mportat parameters are: source ad destato IP addresses, source ad destato port umbers ad status of flags. The dstace betwee the two staces x ad x j [3] s gve by equato below. Ths s the geeral form for calculatg dstace -dmesoal space. d( x, x ) j r r [ a ( x ) a ( x )] r Equato : Euclda dstace betwee two staces wth attrbutes We do ot use ths dstace equato exactly sce we test oly the equalty betwee attrbutes. I earest-eghbor learg, the target fucto may be ether dscrete-valued or real-valued. The form of the dscrete-valued target fucto s f :R ->V, where V {v, v, r j v 3,..., v s } s a fte set ( our case V {regularty, truso}) ad R s real - dmesoal space. The -Nearest Neghbours algorthm for approxmatg a dscrete-valued target fucto [3] s gve algorthm below: Trag part: For For each example <x, <x, f(x)>, add the the example to to the the lst lst of of trag_examples Classfcato part: Gve a ew stace x q q to to be be classfed, Step : : Let Let x,, x,,......,, x deote the the staces from the the trag_examples that that are earest to x q, are earest to x f ˆ q, Step:Retur, ( xq ) arg max δ ( v, f ( x )) v V f ˆ ( xq ) arg max where: f ( a b) δ ( δ ( v, f ( x )) v V a, b) 0 f ( a b) where: f ( a b) δ ( a, b) 0 f ( a b) Algorthm : -earest Neghbours I the trag part we must collect trag examples staces. We collect ther attrbute lst as well as ther target. I our case staces are TCP pacets. We collected the mportat header parameters as a attrbute lst. I the classfcato part we frst search for the -earest staces from the trag examples closest to the actual stace (.e. to the ew stace). The we classfy ths stace accordg to the outcome of the majorty of earest trag staces. Our case s a bt specfc sce all our trag examples are cosdered to be regular,.e. all of them have oly oe outcome. Therefore ew stace s cosdered regular, f ts attrbutes are close eough to the attrbute lsts of trag examples. 3 VAL Statstcal Detector As prevously sad, our statstcal detector performs truso detecto usg adapted IBL wth -Nearest Neghbor method. We collected the trag examples by recordg
3 Ttle of the paper Iformatca 3 (999) xxx yyy 3 the TCP etwor actvty o the computer plugged to uversty departmet traet, for two wees. So collected trag examples were hghly redudat ad osy. To mprove the qualty ad to reduce the sze of gathered data we frst cosdered clusterg methods. Clusterg meas to partto data space to dsjot subsets so that the pots each subset are coheret accordg to a certa crtero. Our dea was to group TCP etwor pacets to sets of smlar pacets ad to preserve oly pacets the ceter of groups. The methods we have spected are: -Meas Mxture of Gaussa dstrbutos used by Expectato-Maxmzato Greedy Clusterg Algorthm 3. -Meas -Meas s oe of the smplest clusterg algorthms. It assumes that the clusters are sphercal, that every cluster has a ceter ad that other pots belogg to the cluster are close aroud the ceter [4]. See Algorthm below. Iput data pots { x, x,.., x} ; umber of clusters Output clust() for ; c, c,.., c postos of ceters Italze c, c,.., c wth radom values Do for.. fd such that x c x c' for all,.., clust() for,.., C { x, clust( ) } c x C C utl clust(),,.., rema uchaged Algorthm : -Meas clusterg We have put data pots ad clusters, whle the output s the assgmet of data pots to clusters ad postos of cluster ceters. Frst, cluster ceters are talzed wth radom values. The, a loop, the data pots are frst assged to the cluster wth the ceter earest to the data pot. I the ext step, the cluster ceters are recalculated from all the pots curretly the cluster. The loop terates utl classfcato of all the data pots to the clusters remas uchaged. The -Meas algorthm fals to fd the correct clusterg whe clusters have dfferet szes ad/or they have (dfferet) elogated shapes [4]. 3. Mxture of Gaussa dstrbutos Dfferet models have to be used for clusters that are t sphercal. Oe of them ca be a mxture of Gaussa dstrbutos. A mxture of Gaussa dstrbutos [4] s a probablty desty gve by f ( x) λ f ( x) where: f (x) are ormal destes wth parameters µ σ called the mxture compoets,, λ 0 are real umbers satsfyg λ, called mxture coeffcets. Itutvely, adoptg a mxture reflects the assumpto that there are sources whch depedetly geerate data ( f, f,.., f ). The probablty that data s geerated by f s λ. So ( λ, λ,.., λ ) represet a dscrete dstrbuto over the sources. The ew data pot s geerated two steps: the frst source f s radomly pced from ( f, f,.., f ) wth a probablty gve by ( λ, λ,.., λ ), the secod data pot x s sampled from chose f. We ow x, but we do t ow, the dex of the source that geerated x. Therefore s called the hdde varable [4]. f (x) ca be rewrtte to show the two-step data geerato model: f ( x) ) f ( x ) where: ) λ for,
4 Ttle of the paper Iformatca 3 (999) xxx yyy 4 f ( x ) f ( x) I ths probablstc framewor, the clusterg problem ca be traslated as follows. Fdg the clusters s equvalet to estmatg the destes of the data sources ( f, f,.., f ). Assgg the data to the clusters meas recoverg the values of the hdde varable for each data pot [4]. 3.3 Expectatos-Maxmzato The Expectato-Maxmzato (EM) algorthm [4][5] solves the clusterg problem as a Maxmum Lelhood estmato problem. It s based o mxture of the Gaussa dstrbutos. It taes the data D { x, x,.., x} ad the umber of clusters as the put ad outputs the model parameters Θ { λ,.., λ, µ,.., µ, σ,.., σ } ad the posteror probablty of the clusters for each data pot γ (), for,,,... For ay gve set of model parameters Θ, we compute the probablty P ( x ) that observato x was geerated by the -th source f usg the Bayes formula x ) ) f ( x ) )' f ( x )' ' ' λ f ( x ) γ ( ) λ f ( x ) ' ' The values γ (),,.., sum to. They are called the partal assgmets of pot x to the -clusters - see Algorthm 3. It ca be proved that the EM algorthm coverges. The parameters Θ obtaed at covergece represet a local maxmum of the lelhood L(Θ). The complexty of each terato s O(). Clusterg methods based o EM are popular because they are geeral ad ofte hghly effectve. However whe may local optma are preset the lelhood space the qualty of the soluto produced ca be sestve to the tal assgmet of pots to clusters. A larger dffculty for the aomaly detecto doma s that, the umber of clusters to be sought must be ow a pror, yet t s ot clear how to determe the umber of atural clusters a set of etwor pacets wth ther parameters. Furthermore, for large search tme ca be prohbtve []. Iput { x, x,.., x} the data pots, the umber of clusters Output γ () for,..,,.., µ, σ for, the parameters of the mxture compoets λ for,, the mxture coeffcets Italze µ, σ, λ for,.., wth radom values Do E step for,..., λ f ( x ) γ ( ) for, λ f ( x M step for, ' γ ( ) ' ' ) λ µ γ ( ) x σ utl covergece Algorthm 3: Expectatos-Maxmzato 3.4 Greedy Clusterg Algorthm Greedy clusterg algorthm [] bulds dvdual clusters cosecutvely attemptg to mmze the crtero: Dst( x, y) x C y C val( C) C for each cluster C. Begg wth the tal pot, the cluster grows by cludg pots, whch creases val(c) the least. Growth s stopped whe the value reaches a local mmum. Whe the cluster s complete we defe ts ceter,.e. the pot, whch has the mmum dstace to all other pots the cluster. Fally, the cluster s represeted oly by the ceter pot ad the mea radus. The complete clusterg algorthm s smlar to the sgle cluster costructo. We γ ( )( x µ )
5 Ttle of the paper Iformatca 3 (999) xxx yyy 5 sequetally select dvdual clusters by ther ablty to maxmze the mea tra-cluster dstace: val{ C, C,.., C } Dst( C,, C j cet j, cet We halt the clusterg process whe the tercluster value falls below a certa threshold. Ths parameter defes whe the clusterg process wll be halted ad how may clusters wll be created. A small threshold results may clusters ad a large oe few clusters. 3.5 Our Algorthm After cosderg all of these clusterg methods ad a umber of etwor pacets collected by recordg a etwor traffc, whch was greater tha 00000, we had to fd a computatoally less demadg algorthm. Frst, we decded to dscard all the pacets whose source IP, destato IP, port umber ad TCP flags combato appeared oly oce the collecto of pacets. After that, we further reduced our collecto by preservg oly oe pacet amog all whch had the same source IP, destato IP, port umber ad TCP flags combato. I ths way, we reduced the umber of pacets to oly about 500 pacets. 3.6 Smlarty Measure Decso about truso based o oly oe pacet s certaly urelable. Therefore, we decded to base the decso whether there s truso or ot by cosderg a sequece of pacets. We cosdered dfferet ds of smlarty fuctos [] to compare the sequeces of pacets. Sce a exact match betwee the volved sequeces s t lely, we examed four varats of loosely matchg smlarty fuctos. Furthermore, we do t requre all header data betwee two pacets (oe from actual sequece ad the other from trag examples sequece) to be the same but at least the source IP, the destato IP, the port ad flags. Frst of the fuctos, deoted as MC-P (Match Cout Polyomal), smply couts the umber of matchg postos betwee the sequeces. ) The ext smlarty fucto s deoted as MC-E (Match Cout Expoetally). Ths fucto doubles ts value for each matchg posto betwee sequeces. The ext two smlarty fuctos are based o the feelg that adjacet matches should have stroger weght. Therefore we explored the MCA-P (Match Cout Adjacecy Polyomal) ad the MCA-E (Match Cout Adjacecy Expoetal) fucto. Smlarty measure computato s the same all four cases (oly fuctos are dfferet) - see Algorthm 4 below. Set a adjacecy couter c to oe (c ) ad the tal value of the smlarty measure to, Sm. For each posto j the sequece legth l: If Xj Yj the Sm f(sm,c) ad c u(c) otherwse c. After all postos are examed retur the measure value. Algorthm 4: Smlarty measure computato We have a sequece of l actual pacets X ( x, x,.., xl ) ad sequece of l trag examples Y ( y, y,.., yl ). Fally, there s table wth f(sm,c) ad u(c) deftos for all four types of smlarty measure see Table below. f(sm,c) u(c) MC-P 0 Sm + MCA-P 0 Sm + c c+ MC-E * Sm MCA-E 0 Sm + c *c Table : Fuctos for dfferet smlarty measure computatos It was foud that statstcal sgfcace of smlarty fuctos s dstgushable, so we used MC-P. 3.7 Other parts of VAL We combed our VAL statstcal detector wth GNU lcesed rule-based lghtweght truso detector SNORT. It s used ot oly for rule-based detecto but t serves also for
6 Ttle of the paper Iformatca 3 (999) xxx yyy 6 TCP etwor traffc capture. Traffc s stored to MySQL database. From there s accessed by the statstcal detector wrtte GNU C. The orgal database schema defed wth SNORT s adjusted ad exteded. I ths way, we produced a hybrd truso detecto system. Furthermore, we corporated Lux ptables persoal frewall to bloc hostle actvtes detected ether wth SNORT or wth the statstcal detector. Addtoally, e-mal s set to the securty admstrator f truso s detected. We also bult a web maagemet cosole wrtte PHP wth access to the same MySQL database for admstratve ad formatve purposes. 4 Results To test the statstcal detector, we used Nessus Vulerablty Scaer ad geerated the attacs ourselves. We executed a whole rage of attacs ad obtaed the followg results: Total umber of pacets was Number of captured pacets was 343 or % Number of ot captured pacets was 4790 or 5.940% Number of detected trusve pacets was 703 or 95.73% (amog captured) Number of udetected trusve pacets was 077 or 4.77% (amog captured) The results are relatvely satsfyg. However, at a greater regular traffc load, the result would probably deterorate. Also, f the attacer goes slow ad low, most lely othg would be detected. However, o statstcal detector performs better smlar codtos. 5 Cocluso Securty threats to our computer systems ca be reduced, wth the help of a truso detecto system. A statstcal detector performs truso detecto by comparg a curret actvty wth a owledge base of regular actvty. Our VAL statstcal detector, uses Istace-based Learg wth the - Nearest Neghbor method. To mprove the qualty of gathered data ad to reduce ts quatty, we examed varous clusterg algorthms ad fally used our ow. The we spected fuctos for smlarty measure computato. Sce t has bee foud that there s o sgfcat dfferece ther qualty we used MC-P. We completed our soluto wth SNORT, the frewall ad the maagemet cosole. The testg has show that our detector, combed wth other compoets, secures computer coected to Iteret qute well despte ts smple costructo. Acowledgemet Authors are thaful to Mha Strehar for sharg hs experece about truso detecto, hs help at etwor traffc acqusto ad soluto testg. Refereces [] Terra Lae, Mache Learg Techques for the Computer Securty: Doma of Aomaly Detecto, A Thess Submtted to the Faculty of Purdue Uversty, 000 [] D. Aha, D. bler, M. Albert: Istace- Based Learg Algorthms, Mache Learg, 99 [3] B. V. Dasarathy: Nearest Neghbor (NN) Norms: NN Patter Classfcato Techques, IEEE Computer Socety Press, 99 [4] D.R. Wlso, T.R. Martez: Reducto Techques for Exemplar-Based Learg Algorthms, Mache Learg, 000 [5] T.. Moo: The Expectato- Maxmzato Algorthm, IEEE Sgal Processg Magaz, 996 [6] Stephe Northcutt: Networ Itruso Detecto: A Aalyst's Hadboo, New Rders, 999
Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology
I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50
More informationIDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki
IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,
More information6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis
6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces
More informationANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data
ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there
More informationSpeeding up k-means Clustering by Bootstrap Averaging
Speedg up -meas Clusterg by Bootstrap Averagg Ia Davdso ad Ashw Satyaarayaa Computer Scece Dept, SUNY Albay, NY, USA,. {davdso, ashw}@cs.albay.edu Abstract K-meas clusterg s oe of the most popular clusterg
More informationAPPENDIX III THE ENVELOPE PROPERTY
Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful
More informationNumerical Methods with MS Excel
TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how
More informationOn Error Detection with Block Codes
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 3 Sofa 2009 O Error Detecto wth Block Codes Rostza Doduekova Chalmers Uversty of Techology ad the Uversty of Gotheburg,
More informationMaintenance Scheduling of Distribution System with Optimal Economy and Reliability
Egeerg, 203, 5, 4-8 http://dx.do.org/0.4236/eg.203.59b003 Publshed Ole September 203 (http://www.scrp.org/joural/eg) Mateace Schedulg of Dstrbuto System wth Optmal Ecoomy ad Relablty Syua Hog, Hafeg L,
More informationCHAPTER 2. Time Value of Money 6-1
CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show
More information1. The Time Value of Money
Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg
More informationADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN
Colloquum Bometrcum 4 ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Zofa Hausz, Joaa Tarasńska Departmet of Appled Mathematcs ad Computer Scece Uversty of Lfe Sceces Lubl Akademcka 3, -95 Lubl
More informationSettlement Prediction by Spatial-temporal Random Process
Safety, Relablty ad Rs of Structures, Ifrastructures ad Egeerg Systems Furuta, Fragopol & Shozua (eds Taylor & Fracs Group, Lodo, ISBN 978---77- Settlemet Predcto by Spatal-temporal Radom Process P. Rugbaapha
More informationChapter Eight. f : R R
Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,
More informationAverage Price Ratios
Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or
More informationA Bayesian Networks in Intrusion Detection Systems
Joural of Computer Scece 3 (5: 59-65, 007 ISSN 549-3636 007 Scece Publcatos A Bayesa Networs Itruso Detecto Systems M. Mehd, S. Zar, A. Aou ad M. Besebt Electrocs Departmet, Uversty of Blda, Algera Abstract:
More informationAn Effectiveness of Integrated Portfolio in Bancassurance
A Effectveess of Itegrated Portfolo Bacassurace Taea Karya Research Ceter for Facal Egeerg Isttute of Ecoomc Research Kyoto versty Sayouu Kyoto 606-850 Japa arya@eryoto-uacp Itroducto As s well ow the
More informationECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil
ECONOMIC CHOICE OF OPTIMUM FEEDER CABE CONSIDERING RISK ANAYSIS I Camargo, F Fgueredo, M De Olvera Uversty of Brasla (UB) ad The Brazla Regulatory Agecy (ANEE), Brazl The choce of the approprate cable
More informationAbraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract
Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected
More informationSHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN
SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN Wojcech Zelńsk Departmet of Ecoometrcs ad Statstcs Warsaw Uversty of Lfe Sceces Nowoursyowska 66, -787 Warszawa e-mal: wojtekzelsk@statystykafo Zofa Hausz,
More informationA New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree
, pp.277-288 http://dx.do.org/10.14257/juesst.2015.8.1.25 A New Bayesa Network Method for Computg Bottom Evet's Structural Importace Degree usg Jotree Wag Yao ad Su Q School of Aeroautcs, Northwester Polytechcal
More informationThe Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev
The Gompertz-Makeham dstrbuto by Fredrk Norström Master s thess Mathematcal Statstcs, Umeå Uversty, 997 Supervsor: Yur Belyaev Abstract Ths work s about the Gompertz-Makeham dstrbuto. The dstrbuto has
More informationPreprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.
Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E
More informationAn IG-RS-SVM classifier for analyzing reviews of E-commerce product
Iteratoal Coferece o Iformato Techology ad Maagemet Iovato (ICITMI 205) A IG-RS-SVM classfer for aalyzg revews of E-commerce product Jaju Ye a, Hua Re b ad Hagxa Zhou c * College of Iformato Egeerg, Cha
More informationApplications of Support Vector Machine Based on Boolean Kernel to Spam Filtering
Moder Appled Scece October, 2009 Applcatos of Support Vector Mache Based o Boolea Kerel to Spam Flterg Shugag Lu & Keb Cu School of Computer scece ad techology, North Cha Electrc Power Uversty Hebe 071003,
More informationSimple Linear Regression
Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8
More informationCredibility Premium Calculation in Motor Third-Party Liability Insurance
Advaces Mathematcal ad Computatoal Methods Credblty remum Calculato Motor Thrd-arty Lablty Isurace BOHA LIA, JAA KUBAOVÁ epartmet of Mathematcs ad Quattatve Methods Uversty of ardubce Studetská 95, 53
More informationT = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :
Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of
More informationA Novel Method in Scam Detection and Prevention using Data Mining Approaches
A Novel Method Scam Detecto ad Preveto usg Data Mg Approaches Maryam Mokhtar, Mohammad Saraee, Alreza Haghsheas Departmet of Electrcal ad Computer Egeerg Isfaha Uversty of Techology, Isfaha, Ira Mokhtar@ec.ut.ac.r,
More informationBayesian Network Representation
Readgs: K&F 3., 3.2, 3.3, 3.4. Bayesa Network Represetato Lecture 2 Mar 30, 20 CSE 55, Statstcal Methods, Sprg 20 Istructor: Su-I Lee Uversty of Washgto, Seattle Last tme & today Last tme Probablty theory
More informationAn Approach to Evaluating the Computer Network Security with Hesitant Fuzzy Information
A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog, Frst ad Correspodg Author
More informationThe simple linear Regression Model
The smple lear Regresso Model Correlato coeffcet s o-parametrc ad just dcates that two varables are assocated wth oe aother, but t does ot gve a deas of the kd of relatoshp. Regresso models help vestgatg
More informationn. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom.
UMEÅ UNIVERSITET Matematsk-statstska sttutoe Multvarat dataaalys för tekologer MSTB0 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multvarat dataaalys för tekologer B, 5 poäg.
More informationThe Digital Signature Scheme MQQ-SIG
The Dgtal Sgature Scheme MQQ-SIG Itellectual Property Statemet ad Techcal Descrpto Frst publshed: 10 October 2010, Last update: 20 December 2010 Dalo Glgorosk 1 ad Rue Stesmo Ødegård 2 ad Rue Erled Jese
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationA Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time
Joural of Na Ka, Vol. 0, No., pp.5-9 (20) 5 A Study of Urelated Parallel-Mache Schedulg wth Deteroratg Mateace Actvtes to Mze the Total Copleto Te Suh-Jeq Yag, Ja-Yuar Guo, Hs-Tao Lee Departet of Idustral
More informationA DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS
L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos A DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS Kwe-Jay L Departmet of Electrcal Egeerg ad Computer Scece
More informationThe analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0
Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may
More information10.5 Future Value and Present Value of a General Annuity Due
Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the
More informationOnline Appendix: Measured Aggregate Gains from International Trade
Ole Appedx: Measured Aggregate Gas from Iteratoal Trade Arel Burste UCLA ad NBER Javer Cravo Uversty of Mchga March 3, 2014 I ths ole appedx we derve addtoal results dscussed the paper. I the frst secto,
More informationGreen Master based on MapReduce Cluster
Gree Master based o MapReduce Cluster Mg-Zh Wu, Yu-Chag L, We-Tsog Lee, Yu-Su L, Fog-Hao Lu Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of
More informationOptimal multi-degree reduction of Bézier curves with constraints of endpoints continuity
Computer Aded Geometrc Desg 19 (2002 365 377 wwwelsevercom/locate/comad Optmal mult-degree reducto of Bézer curves wth costrats of edpots cotuty Guo-Dog Che, Guo-J Wag State Key Laboratory of CAD&CG, Isttute
More informationRUSSIAN ROULETTE AND PARTICLE SPLITTING
RUSSAN ROULETTE AND PARTCLE SPLTTNG M. Ragheb 3/7/203 NTRODUCTON To stuatos are ecoutered partcle trasport smulatos:. a multplyg medum, a partcle such as a eutro a cosmc ray partcle or a photo may geerate
More informationProactive Detection of DDoS Attacks Utilizing k-nn Classifier in an Anti-DDos Framework
World Academy of Scece, Egeerg ad Techology Iteratoal Joural of Computer, Electrcal, Automato, Cotrol ad Iformato Egeerg Vol:4, No:3, 2010 Proactve Detecto of DDoS Attacks Utlzg k-nn Classfer a At-DDos
More informationChapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization
Chapter 3 Mathematcs of Face Secto 4 Preset Value of a Auty; Amortzato Preset Value of a Auty I ths secto, we wll address the problem of determg the amout that should be deposted to a accout ow at a gve
More informationRelaxation Methods for Iterative Solution to Linear Systems of Equations
Relaxato Methods for Iteratve Soluto to Lear Systems of Equatos Gerald Recktewald Portlad State Uversty Mechacal Egeerg Departmet gerry@me.pdx.edu Prmary Topcs Basc Cocepts Statoary Methods a.k.a. Relaxato
More informationBanking (Early Repayment of Housing Loans) Order, 5762 2002 1
akg (Early Repaymet of Housg Loas) Order, 5762 2002 y vrtue of the power vested me uder Secto 3 of the akg Ordace 94 (hereafter, the Ordace ), followg cosultato wth the Commttee, ad wth the approval of
More informationOptimal replacement and overhaul decisions with imperfect maintenance and warranty contracts
Optmal replacemet ad overhaul decsos wth mperfect mateace ad warraty cotracts R. Pascual Departmet of Mechacal Egeerg, Uversdad de Chle, Caslla 2777, Satago, Chle Phoe: +56-2-6784591 Fax:+56-2-689657 rpascual@g.uchle.cl
More informationChapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =
Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are
More informationRobust Realtime Face Recognition And Tracking System
JCS& Vol. 9 No. October 9 Robust Realtme Face Recogto Ad rackg System Ka Che,Le Ju Zhao East Cha Uversty of Scece ad echology Emal:asa85@hotmal.com Abstract here s some very mportat meag the study of realtme
More informationThe Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk
The Aalyss of Developmet of Isurace Cotract Premums of Geeral Lablty Isurace the Busess Isurace Rsk the Frame of the Czech Isurace Market 1998 011 Scetfc Coferece Jue, 10. - 14. 013 Pavla Kubová Departmet
More informationANALYTICAL MODEL FOR TCP FILE TRANSFERS OVER UMTS. Janne Peisa Ericsson Research 02420 Jorvas, Finland. Michael Meyer Ericsson Research, Germany
ANALYTICAL MODEL FOR TCP FILE TRANSFERS OVER UMTS Jae Pesa Erco Research 4 Jorvas, Flad Mchael Meyer Erco Research, Germay Abstract Ths paper proposes a farly complex model to aalyze the performace of
More informationFractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK
Fractal-Structured Karatsuba`s Algorthm for Bary Feld Multplcato: FK *The authors are worg at the Isttute of Mathematcs The Academy of Sceces of DPR Korea. **Address : U Jog dstrct Kwahadog Number Pyogyag
More informationAnalysis of one-dimensional consolidation of soft soils with non-darcian flow caused by non-newtonian liquid
Joural of Rock Mechacs ad Geotechcal Egeerg., 4 (3): 5 57 Aalyss of oe-dmesoal cosoldato of soft sols wth o-darca flow caused by o-newtoa lqud Kaghe Xe, Chuaxu L, *, Xgwag Lu 3, Yul Wag Isttute of Geotechcal
More informationA particle Swarm Optimization-based Framework for Agile Software Effort Estimation
The Iteratoal Joural Of Egeerg Ad Scece (IJES) olume 3 Issue 6 Pages 30-36 204 ISSN (e): 239 83 ISSN (p): 239 805 A partcle Swarm Optmzato-based Framework for Agle Software Effort Estmato Maga I, & 2 Blamah
More informationDynamic Two-phase Truncated Rayleigh Model for Release Date Prediction of Software
J. Software Egeerg & Applcatos 3 63-69 do:.436/jsea..367 Publshed Ole Jue (http://www.scrp.org/joural/jsea) Dyamc Two-phase Trucated Raylegh Model for Release Date Predcto of Software Lafe Qa Qgchua Yao
More informationReport 52 Fixed Maturity EUR Industrial Bond Funds
Rep52, Computed & Prted: 17/06/2015 11:53 Report 52 Fxed Maturty EUR Idustral Bod Fuds From Dec 2008 to Dec 2014 31/12/2008 31 December 1999 31/12/2014 Bechmark Noe Defto of the frm ad geeral formato:
More informationIP Network Topology Link Prediction Based on Improved Local Information Similarity Algorithm
Iteratoal Joural of Grd Dstrbuto Computg, pp.141-150 http://dx.do.org/10.14257/jgdc.2015.8.6.14 IP Network Topology Lk Predcto Based o Improved Local Iformato mlarty Algorthm Che Yu* 1, 2 ad Dua Zhem 1
More informationFast, Secure Encryption for Indexing in a Column-Oriented DBMS
Fast, Secure Ecrypto for Idexg a Colum-Oreted DBMS Tgja Ge, Sta Zdok Brow Uversty {tge, sbz}@cs.brow.edu Abstract Networked formato systems requre strog securty guaratees because of the ew threats that
More informationA Single Machine Scheduling with Periodic Maintenance
A Sgle Mache Schedulg wth Perodc Mateace Fracsco Ágel-Bello Ada Álvarez 2 Joaquí Pacheco 3 Irs Martíez Ceter for Qualty ad Maufacturg, Tecológco de Moterrey, Eugeo Garza Sada 250, 64849 Moterrey, NL, Meco
More informationCIS603 - Artificial Intelligence. Logistic regression. (some material adopted from notes by M. Hauskrecht) CIS603 - AI. Supervised learning
CIS63 - Artfcal Itellgece Logstc regresso Vasleos Megalookoomou some materal adopted from otes b M. Hauskrecht Supervsed learg Data: D { d d.. d} a set of eamples d < > s put vector ad s desred output
More informationwhere p is the centroid of the neighbors of p. Consider the eigenvector problem
Vrtual avgato of teror structures by ldar Yogja X a, Xaolg L a, Ye Dua a, Norbert Maerz b a Uversty of Mssour at Columba b Mssour Uversty of Scece ad Techology ABSTRACT I ths project, we propose to develop
More informationGroup Nearest Neighbor Queries
Group Nearest Neghbor Queres Dmtrs Papadas Qogmao She Yufe Tao Kyrakos Mouratds Departmet of Computer Scece Hog Kog Uversty of Scece ad Techology Clear Water Bay, Hog Kog {dmtrs, qmshe, kyrakos}@cs.ust.hk
More informationNear Neighbor Distribution in Sets of Fractal Nature
Iteratoal Joural of Computer Iformato Systems ad Idustral Maagemet Applcatos. ISS 250-7988 Volume 5 (202) 3 pp. 59-66 MIR Labs, www.mrlabs.et/jcsm/dex.html ear eghbor Dstrbuto Sets of Fractal ature Marcel
More informationPerformance Attribution. Methodology Overview
erformace Attrbuto Methodology Overvew Faba SUAREZ March 2004 erformace Attrbuto Methodology 1.1 Itroducto erformace Attrbuto s a set of techques that performace aalysts use to expla why a portfolo's performace
More informationMDM 4U PRACTICE EXAMINATION
MDM 4U RCTICE EXMINTION Ths s a ractce eam. It does ot cover all the materal ths course ad should ot be the oly revew that you do rearato for your fal eam. Your eam may cota questos that do ot aear o ths
More informationOn formula to compute primes and the n th prime
Joural's Ttle, Vol., 00, o., - O formula to compute prmes ad the th prme Issam Kaddoura Lebaese Iteratoal Uversty Faculty of Arts ad ceces, Lebao Emal: ssam.addoura@lu.edu.lb amh Abdul-Nab Lebaese Iteratoal
More informationDIGITAL AUDIO WATERMARKING: SURVEY
DIGITAL AUDIO WATERMARKING: SURVEY MIKDAM A. T. ALSALAMI * MARWAN M. AL-AKAIDI ** * Computer Scece Dept. Zara Prvate Uversty / Jorda ** School of Egeerg ad Techology - De Motfort Uversty / UK Abstract:
More informationForecasting Trend and Stock Price with Adaptive Extended Kalman Filter Data Fusion
2011 Iteratoal Coferece o Ecoomcs ad Face Research IPEDR vol.4 (2011 (2011 IACSIT Press, Sgapore Forecastg Tred ad Stoc Prce wth Adaptve Exteded alma Flter Data Fuso Betollah Abar Moghaddam Faculty of
More informationNetwork dimensioning for elastic traffic based on flow-level QoS
Network dmesog for elastc traffc based o flow-level QoS 1(10) Network dmesog for elastc traffc based o flow-level QoS Pas Lassla ad Jorma Vrtamo Networkg Laboratory Helsk Uversty of Techology Itroducto
More informationUSEFULNESS OF BOOTSTRAPPING IN PORTFOLIO MANAGEMENT
USEFULNESS OF BOOTSTRAPPING IN PORTFOLIO MANAGEMENT Radovaov Bors Faculty of Ecoomcs Subotca Segedsk put 9-11 Subotca 24000 E-mal: radovaovb@ef.us.ac.rs Marckć Aleksadra Faculty of Ecoomcs Subotca Segedsk
More informationOptimizing Software Effort Estimation Models Using Firefly Algorithm
Joural of Software Egeerg ad Applcatos, 205, 8, 33-42 Publshed Ole March 205 ScRes. http://www.scrp.org/joural/jsea http://dx.do.org/0.4236/jsea.205.8304 Optmzg Software Effort Estmato Models Usg Frefly
More informationCyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), January Edition, 2011
Cyber Jourals: Multdscplary Jourals cece ad Techology, Joural of elected Areas Telecommucatos (JAT), Jauary dto, 2011 A ovel rtual etwork Mappg Algorthm for Cost Mmzg ZHAG hu-l, QIU Xue-sog tate Key Laboratory
More informationRegression Analysis. 1. Introduction
. Itroducto Regresso aalyss s a statstcal methodology that utlzes the relato betwee two or more quattatve varables so that oe varable ca be predcted from the other, or others. Ths methodology s wdely used
More informationSecurity Analysis of RAPP: An RFID Authentication Protocol based on Permutation
Securty Aalyss of RAPP: A RFID Authetcato Protocol based o Permutato Wag Shao-hu,,, Ha Zhje,, Lu Sujua,, Che Da-we, {College of Computer, Najg Uversty of Posts ad Telecommucatos, Najg 004, Cha Jagsu Hgh
More informationEfficient Traceback of DoS Attacks using Small Worlds in MANET
Effcet Traceback of DoS Attacks usg Small Worlds MANET Yog Km, Vshal Sakhla, Ahmed Helmy Departmet. of Electrcal Egeerg, Uversty of Souther Calfora, U.S.A {yogkm, sakhla, helmy}@ceg.usc.edu Abstract Moble
More informationA particle swarm optimization to vehicle routing problem with fuzzy demands
A partcle swarm optmzato to vehcle routg problem wth fuzzy demads Yag Peg, Ye-me Qa A partcle swarm optmzato to vehcle routg problem wth fuzzy demads Yag Peg 1,Ye-me Qa 1 School of computer ad formato
More informationLoss Distribution Generation in Credit Portfolio Modeling
Loss Dstrbuto Geerato Credt Portfolo Modelg Igor Jouravlev, MMF, Walde Uversty, USA Ruth A. Maurer, Ph.D., Professor Emertus of Mathematcal ad Computer Sceces, Colorado School of Mes, USA Key words: Loss
More informationFinito: A Faster, Permutable Incremental Gradient Method for Big Data Problems
Fto: A Faster, Permutable Icremetal Gradet Method for Bg Data Problems Aaro J Defazo Tbéro S Caetao Just Domke NICTA ad Australa Natoal Uversty AARONDEFAZIO@ANUEDUAU TIBERIOCAETANO@NICTACOMAU JUSTINDOMKE@NICTACOMAU
More informationModels of migration. Frans Willekens. Colorado Conference on the Estimation of Migration 24 26 September 2004
Models of mgrato Fras Wllekes Colorado Coferece o the Estmato of Mgrato 4 6 Setember 004 Itroducto Mgrato : chage of resdece (relocato Mgrato s stuated tme ad sace Cocetual ssues Sace: admstratve boudares
More informationA probabilistic part-of-speech tagger for Swedish
A probablstc part-of-speech tagger for Swedsh eter Nlsso Departmet of Computer Scece Uversty of Lud Lud, Swede dat00pe@ludat.lth.se Abstract Ths paper presets a project for mplemetg ad evaluatg a probablstc
More informationSuspicious Transaction Detection for Anti-Money Laundering
Vol.8, No. (014), pp.157-166 http://dx.do.org/10.1457/jsa.014.8..16 Suspcous Trasacto Detecto for At-Moey Lauderg Xgrog Luo Vocatoal ad techcal college Esh Esh, Hube, Cha es_lxr@16.com Abstract Moey lauderg
More informationReinsurance and the distribution of term insurance claims
Resurace ad the dstrbuto of term surace clams By Rchard Bruyel FIAA, FNZSA Preseted to the NZ Socety of Actuares Coferece Queestow - November 006 1 1 Itroducto Ths paper vestgates the effect of resurace
More informationHow To Make A Supply Chain System Work
Iteratoal Joural of Iformato Techology ad Kowledge Maagemet July-December 200, Volume 2, No. 2, pp. 3-35 LATERAL TRANSHIPMENT-A TECHNIQUE FOR INVENTORY CONTROL IN MULTI RETAILER SUPPLY CHAIN SYSTEM Dharamvr
More informationProjection model for Computer Network Security Evaluation with interval-valued intuitionistic fuzzy information. Qingxiang Li
Iteratoal Joural of Scece Vol No7 05 ISSN: 83-4890 Proecto model for Computer Network Securty Evaluato wth terval-valued tutostc fuzzy formato Qgxag L School of Software Egeerg Chogqg Uversty of rts ad
More informationSTOCHASTIC approximation algorithms have several
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 60, NO 10, OCTOBER 2014 6609 Trackg a Markov-Modulated Statoary Degree Dstrbuto of a Dyamc Radom Graph Mazyar Hamd, Vkram Krshamurthy, Fellow, IEEE, ad George
More informationClassic Problems at a Glance using the TVM Solver
C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the
More informationAn Evaluation of Naïve Bayesian Anti-Spam Filtering Techniques
Proceedgs of the 2007 IEEE Workshop o Iformato Assurace Uted tates Mltary Academy, West Pot, Y 20-22 Jue 2007 A Evaluato of aïve Bayesa At-pam Flterg Techques Vkas P. Deshpade, Robert F. Erbacher, ad Chrs
More informationRQM: A new rate-based active queue management algorithm
: A ew rate-based actve queue maagemet algorthm Jeff Edmods, Suprakash Datta, Patrck Dymod, Kashf Al Computer Scece ad Egeerg Departmet, York Uversty, Toroto, Caada Abstract I ths paper, we propose a ew
More informationLearning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach 1
Learg to Flter Spam E-Mal: A Comparso of a Nave Bayesa ad a Memory-Based Approach 1 Io Adroutsopoulos, Georgos Palouras, Vagels Karkaletss, Georgos Sakks, Costate D. Spyropoulos ad Paagots Stamatopoulos
More informationA COMPARATIVE STUDY BETWEEN POLYCLASS AND MULTICLASS LANGUAGE MODELS
A COMPARATIVE STUDY BETWEEN POLYCLASS AND MULTICLASS LANGUAGE MODELS I Ztou, K Smaïl, S Delge, F Bmbot To cte ths verso: I Ztou, K Smaïl, S Delge, F Bmbot. A COMPARATIVE STUDY BETWEEN POLY- CLASS AND MULTICLASS
More informationA Comparative Study for Email Classification
A Coparatve Study for Eal Classfcato Seogwook You ad Des McLeod Uversty of Souther Calfora, Los Ageles, CA 90089 USA Abstract - Eal has becoe oe of the fastest ad ost ecoocal fors of coucato. However,
More informationIntroduction to Maintainability
Itroducto to Mataablty The cocept of mataablty ecompasses: A operatoal measure of effectveess A characterstc of desg A egeerg specalty that supports desg A cost drver A plaed actvty each stage of product
More informationIntegrating Production Scheduling and Maintenance: Practical Implications
Proceedgs of the 2012 Iteratoal Coferece o Idustral Egeerg ad Operatos Maagemet Istabul, Turkey, uly 3 6, 2012 Itegratg Producto Schedulg ad Mateace: Practcal Implcatos Lath A. Hadd ad Umar M. Al-Turk
More informationOptimization Model in Human Resource Management for Job Allocation in ICT Project
Optmzato Model Huma Resource Maagemet for Job Allocato ICT Project Optmzato Model Huma Resource Maagemet for Job Allocato ICT Project Saghamtra Mohaty Malaya Kumar Nayak 2 2 Professor ad Head Research
More informationStudy on prediction of network security situation based on fuzzy neutral network
Avalable ole www.ocpr.com Joural of Chemcal ad Pharmaceutcal Research, 04, 6(6):00-06 Research Artcle ISS : 0975-7384 CODE(USA) : JCPRC5 Study o predcto of etwork securty stuato based o fuzzy eutral etwork
More informationThree Dimensional Interpolation of Video Signals
Three Dmesoal Iterpolato of Vdeo Sgals Elham Shahfard March 0 th 006 Outle A Bref reve of prevous tals Dgtal Iterpolato Bascs Upsamplg D Flter Desg Issues Ifte Impulse Respose Fte Impulse Respose Desged
More informationResearch on Cloud Computing and Its Application in Big Data Processing of Railway Passenger Flow
325 A publcato of CHEMICAL ENGINEERING TRANSACTIONS VOL. 46, 2015 Guest Edtors: Peyu Re, Yacag L, Hupg Sog Copyrght 2015, AIDIC Servz S.r.l., ISBN 978-88-95608-37-2; ISSN 2283-9216 The Itala Assocato of
More informationHow To Value An Annuity
Future Value of a Auty After payg all your blls, you have $200 left each payday (at the ed of each moth) that you wll put to savgs order to save up a dow paymet for a house. If you vest ths moey at 5%
More informationApproximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines
(ICS) Iteratoal oural of dvaced Comuter Scece ad lcatos Vol 6 No 05 romato lgorthms for Schedulg wth eecto o wo Urelated Parallel aches Feg Xahao Zhag Zega Ca College of Scece y Uversty y Shadog Cha 76005
More information