Preventing Information Leakage in C Applications Using RBAC-Based Model

Size: px
Start display at page:

Download "Preventing Information Leakage in C Applications Using RBAC-Based Model"

Transcription

1 Proceedgs of the 5th WSEAS It. Cof. o Software Egeerg Parallel ad Dstrbuted Systems Madrd Spa February (pp69-73) Prevetg Iformato Leakage C Applcatos Usg RBAC-Based Model SHIH-CHIEN CHOU Departmet of Computer Scece ad Iformato Egeerg Natoal Dog Hwa Uversty Secto 2 Da-Hsueh Road Shoufog Huale 974 TAIWAN Abstract - Whe a applcato s beg executed users ca read the applcato s output. If sestve formato s maaged by a applcato formato should be preveted from beg leaked to uauthorzed users durg applcato executo. The preveto ca be acheved through formato flow cotrol. Sce the procedural C laguage s stll use heavly we developed a model based o role-based access cotrol (RBAC) for C applcatos. Ths paper descrbes the model. Key-words: Iformato securty formato flow cotrol role-based access cotrol (RBAC). Itroducto Whe a applcato s beg executed users play roles. A user playg a role ca access the applcato s output. If a applcato maages sestve formato prevetg formato leakage durg applcato executo s mportat. Iformato leakage refers to leakg hgh securty level formato to low securty level users. To prevet formato leakage formato flow cotrol models ca be used []. Sce the C laguage s stll use heavly we developed a formato flow cotrol model CRBAC for C based o role-based access cotrol (RBAC) [2]. CRBAC cotrols both read ad wrte access ad offers the followg addtoal features: a. Prevetg drect formato leakage. Ths leakage refers to leakg formato through the thrd oe(s). Geerally ths leakage ca be preveted usg jo operatg [3]. b. Maagg user relatoshps. User relatoshps may affect permssos whe users play roles a applcato. For example suppose freds ca read oe aother s geeral formato such as age. Also suppose that Mary ad Joh are freds. The Mary ad Joh ca read each other s geeral formato. Whe they break fredshp they ca o loger read oe aother s geeral formato. Sce user relatoshps may chage durg rutme user permssos should be chaged accordg to user relatoshp chage. c. Correctg permssos valdated by user relatoshp chage. User relatoshp chage may affect the preveto of drect formato leakage. For example suppose Tom ad Mary are tally ot freds. The Tom caot read Mary s geeral formato. Suppose at ths tme the formato ageset s derved from Mary s age ad others ages that ca be read by Tom. The accordg to the jo operato Tom s ot allowed to read ageset. If Tom ad Mary become freds after a certa tme (user relatoshp chage occurs ths case) Tom ca read Mary s age ths tme. I ths case should Tom be allowed to read the formato ageset produced before? The aswer should be yes because: () Tom ca read Mary s age after the user relatoshp chage ad (2) ageset s derved from Mary s age ad others ages that ca be read by Tom. Sce Tom ca read all the ages that derved ageset after the user relatoshp chage Tom should be allowed to read ageset after the chage. Allowg Tom to read ageset valdates the prevous jo operato because the prevous jo operatos dsallowed Tom to read ageset. The valdato requres prevous jo operatos to be corrected. d. Avodg mproper fucto call. Dfferet fuctos a C applcato may be dfferet securty levels ad therefore should be protected depedetly. Ths paper presets CRBAC ad ts evaluato. 2. Related Work RBAC s useful access cotrol. Nevertheless sce the orgal desg of RBAC s ot for formato flow cotrol most features metoed secto are ot offered by the geeral cases of RBAC. The model [4] uses

2 Proceedgs of the 5th WSEAS It. Cof. o Software Egeerg Parallel ad Dstrbuted Systems Madrd Spa February (pp69-73) RBAC to cotrol formato flows wth object-oreted systems. It classfes object methods ad derves a flow graph from method vocatos. From the graph o-secure formato flows ca be detfed. The model [5] uses access cotrol lsts (ACLs) of objects to compute ACLs of executos (whch are composed of oe or more methods). A message flter s used to flter out possbly o-secure formato flows. Flexblty s added by allowg exceptos durg or after method executo [6]. More flexblty s added usg versos [7]. The decetralzed label approach [3] marks the securty levels of ables usg labels. A label s composed of oe or more polces whch should be smultaeously obeyed. A polcy a label s composed of a ower ad zero or more readers that are allowed to read the data. Jo operato s used to prevet drect formato leakage. Wrte access s cotrolled. CACL [8] s our prevous work. It caot maage user relatoshps ad adjust permssos valdated by user relatoshp chage. 3. CRBAC The major problem we ecoutered developg CRBAC s What should be regarded as roles a C program? A C fucto s a caddate for a role. Nevertheless a C fucto may allow more tha oe type of users to access ad the users may be dfferet securty levels. If a fucto s regarded as a role users dfferet securty levels ca access formato maaged by the fucto whch may result formato leakage. For example suppose the fucto getifo gets a user s formato. The the followg two cases of formato leakage may happe (suppose a patet s allowed to retreve hs ow formato oly). Frst a patet ca use the fucto to retreve a doctor s formato. Secod a patet ca use the fucto to retreve aother patet s formato. Although formato may be leaked whe regardg fuctos as roles CRBAC stll regards fuctos as roles. Nevertheless the followg requremets should be fulflled for a C program: RleReq. Every fucto a C program s allowed to access by oly oe type of users. Ths solves the problem resulted by the frst case metoed above. RleReq 2. Costrats should be establshed for users to access formato wth a fucto. For example accouts ad passwords should be gve to patets that wll access formato through the fucto getifo. Ths solves the problem resulted by the secod case metoed above. 3. Defto A C applcato Cap embedded wth CRBAC s defed below: Defto. Cap = (USR RLE PER DSR CSG A RPA VFC JH) whch a. USR s the set of users that operate Cap. b. RLE s a set of roles. A role rle correspods to a fucto Cap. c. s a set of user relatoshps. A user relatoshp ur (2 USR - φ ). d. PER s a set of permssos. A permsso s a access rght. CRBAC attaches access rghts to ables because ables carry formato maaged by a applcato. We mplemeted a permsso as a access cotrol lst (ACL). A ACL s composed of a read access cotrol lst (RACL) ad a wrte access cotrol lst (WACL). The ACL ACL assocated wth the able s defed as ACL = (RACL WACL ) whch: () RACL 2 USRxRLE whch x represets Cartesa product. Sce multple users may play the same role RACL has ths defto to dstgush users. A user playg a role RACL s allowed to read. (2) WACL 2 USRxRLE. A user playg a role WACL s allowed to wrte. (3) ( 2 φ ). RACL ad WACL are vald a user relatoshp ur f ur. e. DSR s a set of data sources (DSOCE). The DSOCE of a able records the fuctos that wrote the able s data. f. CSG s a set of CRBAC segmets. A C applcato may have blocks ad the same able ames ca be used dfferet blocks. CRBAC offer CSG to dfferetate ables wth the same ames. g. A s a set of user-role assgmets RLE whch s defed as USR 2. h. RPA s a set of role-permsso assgmets PER whch s defed as RLE 2.. VFC s a set of vald fucto calls. If the fucto f s allowed to voke f2 the elemet (f f2) belogs to VFC. j. JH records jo hstores. It facltates

3 Proceedgs of the 5th WSEAS It. Cof. o Software Egeerg Parallel ad Dstrbuted Systems Madrd Spa February (pp69-73) redog jo operatos to correct permssos (see secto 3.3 for detals). 3.2 Iformato flow securty CRBAC A formato flow occurs whe the result of a computato s assged to a able. To esure secure formato flows both drect ad drect formato flows should be secure. Drect formato flows clude those amog fuctos ad those wth fuctos. Those amog fuctos are duced by fucto calls. If the fucto f vokes f2 a vfc (f f2) should exst. Suppose the vocato s allowed. The the ACLs ad DSOCEs of argumets should be coped to the correspodg parameters. Ths copyg s ecessary because a parameter recevg a argumet herts the securty level of the argumet. Whe the value derved from ables the set { s a able ad s betwee ad } s assged to the able d_ the formato flow duced by the dervato s cosdered secure oly whe both the followg two secure flow codtos are true. To defe the codtos we let: (a) The ACL ad DSOCE of d_ be respectvely (RACL d_ WACL d_ d_ ) ad DSOCE d_. (b) The ACL ad DSOCE of be respectvely ( RACL ) ad DSOCE. WACL Frst secure flow codto: sub ( RACL d_ = = RACL d_ ) so that Secod secure flow codto: sub ( WACL d_ = = DSOCE d_ ) so that The frst codto cotrols read access. The codto RACL d_ RACL = requres that d_ should be the same restrcted as or more restrcted tha the ables the set { s a able ad s betwee ad }. Sce RACLs ad WACLs are vald uder certa user relatoshps the ACL of d_ ad those of the ables the able set metoed above should be vald certa user relatoshp(s). Ths results the requremet sub ( = d_ ). The secod secure flow codto cotrols wrte access. It requres that the data sources of the ables dervg the value assged to d_ should be wth WACL d_ because the data derved from the ables are wrtte to d_. After assgg the derved value to d_ the ACL of d_ should be chaged by the jo operato to prevet drect formato leakage. We use the symbol to represet the operato ad chage ACL d_ to ACL. = Defto 2. WACL = = = ACL = ( ) = RACL I addto to jog ACLs the DSOCE of d_ wll be adjusted to DSOCE. = 3.3 Redog jo operatos ACLs valdated by user relatoshp chage should be corrected by redog jo operatos. Suppose a able d_ s derved from the ables the set VAR. Sce a able may be derved from other ables we suppose that the ables dervg the ables VAR costtute the set VAR 2 the ables dervg the able VAR 2 costtute the set VAR 3 ad so o. The dervato process results rpple effects. The effects ed whe VAR m VAR k whch k < m. We let UVAR be the set = VAR d_ ad suppose that the earlest tme the able beg a derved able s t whch UVAR. From t dow to the curret tme every jo operato whch s a derved able should be redoe. Whe redog jo operatos the curret user relatoshps should be used as a referece because ACLs should be correct uder the curret user relatoshps. The redog should use the compoet JH. Defto 3. A elemet jh of JH Defto s defed below: jh = (t d_ {( ACL ) s a able 2

4 Proceedgs of the 5th WSEAS It. Cof. o Software Egeerg Parallel ad Dstrbuted Systems Madrd Spa February (pp69-73) that derves d_ ad ACL s the ACL of at the tme t} tag) whch a. t s the tme that a jo operato s doe. b. d_ s the derved able. c. {( ACL )} s the set of able ad ther ACLs that derve d_. d. If tag s set t s the earlest tme that d_ s a derved able. Wth the above descrpto the redog of jo operatos s acheved usg Algorthm. Algorthm. Jo operato redog algorthm. Iput data:.. VAR = { s a able to derve d_}.2. d_: the able derved from the able the set VAR 2. Algorthm: 2.. Backtrack JH to detfy VAR 2 through VAR followg the procedure descrbed the frst paragraph of ths secto Let UVAR be the set = VAR d_ For each UVAR do Backtrack JH to detfy the earlest tme t that s a derved able. The tags JH (see Defto 3) facltate the detfcato From the tme t dow to the curret tme mark the jo operatos JH whch s a derved able Ed do 2.5. Redo the marked jo operatos from the earlest tme a jo operato s marked dow to the curret tme. 4. Features Cotrollg both read ad wrte access s acheved by the secure flow codtos. Below we prove that CRBAC offers other features. Lemma : CRBAC prevets drect formato leakage. Proof: Idrect formato leakage results whe a role f2 leaks to f3 the formato retreved from f whch f2 s allowed to read the formato of f whereas f3 ot. To prove that drect formato leakage s avoded we let be a able f that ca be read by the roles s RACL RACL. Accordg to the above assumpto f2 s RACL but f3 ot. We also let 2 be a able f2 whose value s derved from. After the dervato 2 s RACL RACL 2 s modfed by the jo operato (see Defto 2). Suppose drect formato leakage exsts amog f f2 ad f3. Wthout loss of geeralty we assume that f3 ca read 2 after 2 s derved from. Wth ths assumpto f3 s wth RACL 2. However accordg to the jo operato RACL 2 s the tersecto of RACL ad other RACLs after 2 s derved from. Sce f3 s ot RACL f3 s ot RACL 2. # Lemma 2. CRBAC maages user relatoshps. Proof: To prove that CRBAC maages user relatoshps we have to prove that: (a) CRBAC chages role permssos whe user relatoshp chages ad (b) CRBAC corrects permssos valdated by user relatoshp chage. The proof for tem b s Lemma 3. Below we prove that CRBAC chages role permssos whe user relatoshp chages. The followg cases ca be regarded as user relatoshp chage: (a) a system possesses dfferet user relatoshps at dfferet tme say t ad t2 ad (b) a system possesses the same user relatoshps at t ad t2 but at least oe user relatoshp at dfferet tme possesses dfferet roles. Case a: Let t2 = t {ur} whch ur s a user relatoshp ad ur t. I ths case PER t PER t2 whch PER t ad PER t2 are respectvely the permsso sets of the executg system at t ad t2. PER t PER t2 because PER ur PER t2 but PER ur PER t whch PER ur cossts of permssos of users the user relatoshp ur. Case b: Assume that: () ur t ad ur t2 are the same user relatoshp cotag dfferet users at tme t ad t2 ad (2) ur t2 = ur t {u} whch u s a user ad u ur t. I ths case PER t PER t2 because u s the user relatoshp at tme t2 but ot at t whch makes PER t2 to possess more permssos tha PER t. The extra permssos of PER t2 are offered by u. # Lemma 3. CRBAC corrects permssos valdated by user relatoshp chage (.e. Algorthm s correct). Proof. Suppose oly oe able d_ s wth UVAR le 2.2 of Algorthm. The d_ ever plays the role of a derved able. I ths case d_ s ACL s uchaged durg applcato executo. A uchaged ACL s correct because a ACL may be valdated oly whe user relatoshps chage ad the ACL s chaged by jo operato. Suppose Algorthm s correct whe there are (k-) elemets the set UVAR whch 3

5 Proceedgs of the 5th WSEAS It. Cof. o Software Egeerg Parallel ad Dstrbuted Systems Madrd Spa February (pp69-73) UVAR = { s a able s betwee ad (k-) ad (k-) > }. The correctess of Algorthm uder the above assumpto mples that before the able d_ s derved usg the ables UVAR Algorthm corrects ACLs assocated wth the ables UVAR by referrg to the curret user relatoshps. Let s add a able k to the orgal UVAR (we let NewUVAR = UVAR { k }). Accordg to the assumpto the prevous paragraph the ACLs assocated wth the ables NewUVAR excludg k are correct after jo redog. Moreover the ACL assocated wth k s correct because les 2.3 through 2.5 of Algorthm corrects the ACL of k. # Lemma 4. CRBAC avods mproper fucto calls. Proof. A mproper fucto call from the fucto f to f2 may occur whe: (a) f s ot allowed to voke f2 ad (b) f passes mproper argumets to f2. If codto a s true the VFC defed Defto wll block the fucto call. If codto b s true the two secure flow codtos wll block the statemet. # 5. Evaluato A C applcato embedded wth CRBAC model should frst be processed by the CRBAC preprocessor. The output of the preprocessor s a pure C program. The C program geerated by the CRBAC preprocessor s composed of the orgal program ad a securty motor to check formato flow securty durg rutme. We traed studets to use CRBAC. We the requred them to program a smplfed lbrary maagemet system ad a smplfed vetory maagemet system of a supermarket. Durg the programmg we requred the studets to ject user relatoshp chages ad o-secure statemets (o-secure statemets are those that cause o-secure formato flows). We the requred the studets to ru ther programs. The expermets showed that every jected o-secure statemet was detfed. 6. Cocluso Iformato flow cotrol wth a applcato durg ts executo prevets formato leakage. Sce the C laguage s stll use heavly we developed a RBAC-based model CRBAC to cotrol formato flows wth C applcatos. It offers the followg features: a. Cotrollg both read ad wrte accesses usg the two secure flow codtos. b. Prevetg drect formato leakage usg jo operato. c. Maagg user relatoshps. CRBAC uses user relatoshps to lmt permssos so that chagg user relatoshps wll chage user permssos. d. Correctg permssos valdated by user relatoshp chage. CRBAC records jo hstores ad redoes jo operatos to correct permssos usg Algorthm. e. Avodg mproper fucto calls by recordg vald calls. Refereces [] A. Sabelfeld ad A. C. Myers Laguage-Based Iformato-Flow Securty IEEE Joural o Selected Areas Commucatos vol. 2 o. pp [2] R. S. Sadhu E. J. Coye H. L. Feste adc. E. Youma Role-Based Access Cotrol Models IEEE Computer vol. 29 o. 2 pp [3] A. Myers ad B. Lskov Protectg Prvacy usg the Decetralzed Label Model ACM Tras. Software Eg. Methodology vol. 9 o. 4 pp [4] K. Izak K. Taaka ad M. Takzawa frmato Flow Cotrol Role-Based Model for Dstrbuted Objects Proc. 8 th Iteratoal Cof. Parallel ad Dstrbuted Systems pp [5] P. Samarat E. Berto A. Campchett ad S. Jajoda frmato Flow Cotrol Object-Oreted Systems IEEE Tras. Kowledge Data Eg. vol. 9 o. 4 pp Jul./Aug [6] E. Berto Sabra de Capta d Vmercat E. Ferrar ad P. Samarat Excepto-based Iformato Flow Cotrol Object-Oreted Systems ACM Tras. Iformato System Securty vol. o. pp [7] A. Maamr ad A. Fellah Addg Flexblty Iformato Flow Cotrol for Object-Oreted Systems Usg Versos Iteratoal Joural of Software Egeerg ad Kowledge Egeerg vol. 3 o. 3 pp [8] Shh-Che Chou ad Ch-Y Chag A Iformato Flow Cotrol Model for C Applcatos Based o Access Cotrol Lsts Joural of Systems ad Software vol. 78 o. pp Oct

MANAGING THE INTERTWINING AMONG USERS, ROLES, PERMISSIONS, AND USER RELATIONSHIPS IN AN INFORMATION FLOW CONTROL MODEL

MANAGING THE INTERTWINING AMONG USERS, ROLES, PERMISSIONS, AND USER RELATIONSHIPS IN AN INFORMATION FLOW CONTROL MODEL Iteratoal Joural of Securty, Prvacy ad Trust Maagemet ( IJSPTM) vol 2, No 2, Aprl 2013 MANAGING THE INTERTWINING AMONG USERS, ROLES, PERMISSIONS, AND USER RELATIONSHIPS IN AN INFORMATION FLOW CONTROL MODEL

More information

Bezier curves. 1. Defining a Bezier curve. A closed Bezier curve can simply be generated by closing its characteristic polygon

Bezier curves. 1. Defining a Bezier curve. A closed Bezier curve can simply be generated by closing its characteristic polygon Curve represetato Copyrght@, YZU Optmal Desg Laboratory. All rghts reserved. Last updated: Yeh-Lag Hsu (--). Note: Ths s the course materal for ME55 Geometrc modelg ad computer graphcs, Yua Ze Uversty.

More information

Area and Power Efficient Modulo 2^n+1 Multiplier

Area and Power Efficient Modulo 2^n+1 Multiplier Iteratoal Joural of Moder Egeerg Research (IJMER) www.jmer.com Vol.3, Issue.3, May-Jue. 013 pp-137-1376 ISSN: 49-6645 Area ad Power Effcet Modulo ^+1 Multpler K. Ptambar Patra, 1 Saket Shrvastava, Sehlata

More information

NEURO FUZZY MODELING OF CONTROL SYSTEMS

NEURO FUZZY MODELING OF CONTROL SYSTEMS NEURO FUZZY MODELING OF CONTROL SYSTEMS Efré Gorrosteta, Carlos Pedraza Cetro de Igeería y Desarrollo Idustral CIDESI, Av Pe de La Cuesta 70. Des. Sa Pablo. Querétaro, Qro, Méxco gorrosteta@teso.mx pedraza@cdes.mx

More information

COMSC 2613 Summer 2000

COMSC 2613 Summer 2000 Programmg II Fal Exam COMSC 63 Summer Istructos: Name:. Prt your ame the space provded Studet Id:. Prt your studet detfer the space Secto: provded. Date: 3. Prt the secto umber of the secto whch you are

More information

Enumerating XML Data for Dynamic Updating

Enumerating XML Data for Dynamic Updating Eumeratg XML Data for Dyamc Updatg Lau Ho Kt ad Vcet Ng Departmet of Computg, The Hog Kog Polytechc Uversty, Hug Hom, Kowloo, Hog Kog cstyg@comp.polyu.edu.h Abstract I ths paper, a ew mappg model, called

More information

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications /03/ Mache Learg: Algorthms ad Applcatos Florao Z Free Uversty of Boze-Bolzao Faculty of Computer Scece Academc Year 0-0 Lecture 3: th March 0 Naïve Bayes classfer ( Problem defto A trag set X, where each

More information

On a Sufficient and Necessary Condition for Graph Coloring

On a Sufficient and Necessary Condition for Graph Coloring Ope Joural of Dscrete Matheatcs, 04, 4, -5 Publshed Ole Jauary 04 (http://wwwscrporg/joural/ojd) http://dxdoorg/0436/ojd04400 O a Suffcet ad Necessary Codto for raph Colorg Maodog Ye Departet of Matheatcs,

More information

Evaluation of Node and Link Importance Based on Network Topology and Traffic Information DU Xun-Wei, LIU Jun, GUO Wei

Evaluation of Node and Link Importance Based on Network Topology and Traffic Information DU Xun-Wei, LIU Jun, GUO Wei Advaced Materals Research Submtted: 2014-08-29 ISSN: 1662-8985, Vols. 1049-1050, pp 1765-1770 Accepted: 2014-09-01 do:10.4028/www.scetfc.et/amr.1049-1050.1765 Ole: 2014-10-10 2014 Tras Tech Publcatos,

More information

Differentiated Service of Streaming Media Playback Technology

Differentiated Service of Streaming Media Playback Technology Iteratoal Coferece o Advaced Iformato ad Commucato Techology for Educato (ICAICTE 2013) Dfferetated Servce of Streamg Meda Playback Techology CHENG Z-ao 1 MENG Bo 1 WANG Da-hua 1 ZHAO Yue 1 1 Iformatzato

More information

COMBINATORIAL METHOD OF POLYNOMIAL EXPANSION OF SYMMETRIC BOOLEAN FUNCTIONS

COMBINATORIAL METHOD OF POLYNOMIAL EXPANSION OF SYMMETRIC BOOLEAN FUNCTIONS COMBINATORIAL MTHOD O POLYNOMIAL XPANSION O SYMMTRIC BOOLAN UNCTIONS Dala A. Gorodecky The Uted Isttute of Iformatcs Prolems of Natoal Academy of Sceces of Belarus, Msk,, Belarus, dala.gorodecky@gmal.com.

More information

Process Quality Evaluation based on Maximum Entropy Principle. Yuhong Wang, Chuanliang Zhang, Wei Dai a and Yu Zhao

Process Quality Evaluation based on Maximum Entropy Principle. Yuhong Wang, Chuanliang Zhang, Wei Dai a and Yu Zhao Appled Mechacs ad Materals Submtted: 204-08-26 ISSN: 662-7482, Vols. 668-669, pp 625-628 Accepted: 204-09-02 do:0.4028/www.scetfc.et/amm.668-669.625 Ole: 204-0-08 204 Tras Tech Publcatos, Swtzerlad Process

More information

PERSPECTIVES OF THE USE OF GENETIC ALGORITHMS IN CRYPTANALYSIS

PERSPECTIVES OF THE USE OF GENETIC ALGORITHMS IN CRYPTANALYSIS PERSPECTIVES OF THE USE OF GENETIC ALGORITHMS IN CRYPTANALYSIS Lal Besela Sokhum State Uversty, Poltkovskaa str., Tbls, Georga Abstract Moder cryptosystems aalyss s a complex task, the soluto of whch s

More information

Eight Solved and Eight Open Problems in Elementary Geometry

Eight Solved and Eight Open Problems in Elementary Geometry Eght Solved ad Eght Ope Problems Elemetary Geometry Floret Smaradache Math & Scece Departmet Uversty of New Mexco, Gallup, US I ths paper we revew eght prevous proposed ad solved problems of elemetary

More information

Performance Impact of Load Balancers on Server Farms

Performance Impact of Load Balancers on Server Farms erformace Impact of Load Balacers o Server Farms Ypg Dg BMC Software Server Farms have gaed popularty for provdg scalable ad relable computg / Web servces. A load balacer plays a key role ths archtecture,

More information

Journal of Chemical and Pharmaceutical Research, 2015, 7(3): Research Article

Journal of Chemical and Pharmaceutical Research, 2015, 7(3): Research Article Avalable ole www.ocpr.com Joural of Chemcal ad Pharmaceutcal Research, 2015, 73):476-481 Research Artcle ISSN : 0975-7384 CODENUSA) : JCPRC5 Research o cocept smlarty calculato method based o sematc grd

More information

Mode Changes in Priority Pre-emptively Scheduled Systems. K. W. Tindell, A. Burns, A. J. Wellings

Mode Changes in Priority Pre-emptively Scheduled Systems. K. W. Tindell, A. Burns, A. J. Wellings ode hages rorty re-emptvely Scheduled Systems. W. dell, A. Burs, A.. Wellgs Departmet of omputer Scece, Uversty of York, Eglad Abstract may hard real tme systems the set of fuctos that a system s requred

More information

1-D matrix method. U 4 transmitted. incident U 2. reflected U 1 U 5 U 3 L 2 L 3 L 4. EE 439 matrix method 1

1-D matrix method. U 4 transmitted. incident U 2. reflected U 1 U 5 U 3 L 2 L 3 L 4. EE 439 matrix method 1 -D matrx method We ca expad the smple plae-wave scatterg for -D examples that we ve see to a more versatle matrx approach that ca be used to hadle may terestg -D problems. The basc dea s that we ca break

More information

Biconnected Components

Biconnected Components Presetato for use wth the textbook, Algorthm Desg ad Applcatos, by M. T. Goodrch ad R. Tamassa, Wley, 2015 Bcoected Compoets SEA PVD ORD FCO SNA MIA 2015 Goodrch ad Tamassa Bcoectvty 1 Applcato: Networkg

More information

Optimal Allocation of Complex Equipment System Maintainability

Optimal Allocation of Complex Equipment System Maintainability Optmal Allocato of Complex Equpmet System ataablty X Re Graduate School, Natoal Defese Uversty, Bejg, 100091, Cha edcal Protecto Laboratory, Naval edcal Research Isttute, Shagha, 200433, Cha Emal:rexs841013@163.com

More information

A Genetic K-means Clustering Algorithm Applied to Gene Expression Data

A Genetic K-means Clustering Algorithm Applied to Gene Expression Data A Geetc K-meas Clusterg Algorthm Appled to Gee Expresso Data Fag-Xag Wu, W. J. Zhag, ad Athoy J. Kusal Dvso of Bomedcal Egeerg, Uversty of Sasatchewa, Sasatoo, S S7N 5A9, CANADA faw34@mal.usas.ca, zhagc@egr.usas.ca

More information

A Perfect Load Balancing Algorithm on Cube-Connected Cycles

A Perfect Load Balancing Algorithm on Cube-Connected Cycles Proceedgs of the 5th WSEAS It Cof o COMPUTATIONAL INTELLIGENCE, MAN-MACINE SYSTEMS AND CYBERNETICS, Vece, Italy, November -, 6 45 A Perfect Load Balacg Algorthm o Cube-Coected Cycles Gee Eu Ja Shao-We

More information

Clustering documents with vector space model using n-grams

Clustering documents with vector space model using n-grams Clusterg documets wth vector space model usg -grams Klas Skogmar, d97ksk@efd.lth.se Joha Olsso, d97jo@efd.lth.se Lud Isttute of Techology Supervsed by: Perre Nugues, Perre.Nugues@cs.lth.se Abstract Ths

More information

Virtual Machine Allocation in Cloud Computing Environment

Virtual Machine Allocation in Cloud Computing Environment Iteratoal Joural of Cloud Applcatos ad Computg, 3(2), 47-60, Aprl-Jue 203 47 Vrtual Mache Allocato Cloud Computg Evromet Absalom E. Ezugwu, Departmet of Computer Scece, Faculty of Scece, Federal Uversty

More information

APR 1965 Aggregation Methodology

APR 1965 Aggregation Methodology Sa Joaqu Valley Ar Polluto Cotrol Dstrct APR 1965 Aggregato Methodology Approved By: Sged Date: March 3, 2016 Araud Marjollet, Drector of Permt Servces Backgroud Health rsk modelg ad the collecto of emssos

More information

Nine Solved and Nine Open Problems in Elementary Geometry

Nine Solved and Nine Open Problems in Elementary Geometry Ne Solved ad Ne Ope Problems Elemetary Geometry Floret Smaradache Math & Scece Departmet Uversty of New Mexco, Gallup, US I ths paper we revew e prevous proposed ad solved problems of elemetary D geometry

More information

A MapReduce-Based Multiple Flow Direction Runoff Simulation

A MapReduce-Based Multiple Flow Direction Runoff Simulation A MapReduce-Based Multple Flow Drecto Ruoff Smulato Ahmed Sdahmed ad Gyozo Gdofalv GeoIformatcs, Urba lag ad Evromet, KTH Drottg Krstas väg 30 100 44 Stockholm Telephoe: +46-8-790 8709 Emal:{sdahmed, gyozo}@

More information

Vertex Odd Divisor Cordial Labeling of Graphs

Vertex Odd Divisor Cordial Labeling of Graphs IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue 0, October 0. www.jset.com Vertex Odd Dvsor Cordal Labelg of Graphs ISSN 48 68 A. Muthaya ad P. Pugaleth Assstat Professor, P.G.

More information

A Comparison of Heuristics for Scheduling Spatial Clusters to Reduce I/O Cost in Spatial Join Processing

A Comparison of Heuristics for Scheduling Spatial Clusters to Reduce I/O Cost in Spatial Join Processing Edth Cowa Uversty Research Ole ECU Publcatos Pre. 20 2006 A Comparso of Heurstcs for Schedulg Spatal Clusters to Reduce I/O Cost Spatal Jo Processg Jta Xao Edth Cowa Uversty 0.09/ICMLC.2006.258779 Ths

More information

Eight Solved and Eight Open Problems in Elementary Geometry

Eight Solved and Eight Open Problems in Elementary Geometry Eght Solved ad Eght Ope Problems Elemetary Geometry Floret Smaradache Math & Scece Departmet Uversty of New Mexco, Gallup, US I ths paper we revew eght prevous proposed ad solved problems of elemetary

More information

A Comparison of Univariate Smoothing Models: Application to Heart Rate Data Marcus Beal, Member, IEEE

A Comparison of Univariate Smoothing Models: Application to Heart Rate Data Marcus Beal, Member, IEEE A Comparso of Uvarate Smoothg Models: Applcato to Heart Rate Data Marcus Beal, Member, IEEE E-mal: bealm@pdx.edu Abstract There are a umber of uvarate smoothg models that ca be appled to a varety of olear

More information

ITEM ToolKit Technical Support Notes

ITEM ToolKit Technical Support Notes ITEM ToolKt Notes Fault Tree Mathematcs Revew, Ic. 2875 Mchelle Drve Sute 300 Irve, CA 92606 Phoe: +1.240.297.4442 Fax: +1.240.297.4429 http://www.itemsoft.com Page 1 of 15 6/1/2016 Copyrght, Ic., All

More information

Weighting Cache Replace Algorithm for Storage System

Weighting Cache Replace Algorithm for Storage System Weghtg Cache Replace Algorthm for Storage System Yhu Luo 2 Chagsheg Xe 2 Chegfeg Zhag 2 School of mathematcs ad Computer Scece, Hube Uversty, Wuha 430062, P.R. Cha 2 Natoal Storage System Laboratory, School

More information

Grid Resource Discovery Algorithm Based on Distance

Grid Resource Discovery Algorithm Based on Distance 966 JOURNAL OF SOFTWARE, OL. 9, NO., NOEMBER 4 Grd Resource Dscovery Algorthm Based o Dstace Zhogpg Zhag,, Log He, Chao Zhag The School of Iformato Scece ad Egeerg, Yasha Uversty, Qhuagdao, Hebe, 664,

More information

LP: example of formulations

LP: example of formulations LP: eample of formulatos Three classcal decso problems OR: Trasportato problem Product-m problem Producto plag problem Operatos Research Massmo Paolucc DIBRIS Uversty of Geova Trasportato problem The decso

More information

Workflow- Based Shape Optimization of Airfoils and Blades using Chained Bezier Curves

Workflow- Based Shape Optimization of Airfoils and Blades using Chained Bezier Curves Workflow- Based Shape Optmzato of Arfols ad Blades usg Chaed Bezer Curves Igor Pehec, Damr Vuča, Želja Loza Faculty of Electrcal Egeerg, Mechacal Egeerg ad Naval Archtecture FESB, Uversty of Splt, Croata

More information

MATHEMATICAL PROGRAMMING MODEL OF THE CRITICAL CHAIN METHOD

MATHEMATICAL PROGRAMMING MODEL OF THE CRITICAL CHAIN METHOD MATHEMATICAL PROGRAMMING MODEL OF THE CRITICAL CHAIN METHOD TOMÁŠ ŠUBRT, PAVLÍNA LANGROVÁ CUA, SLOVAKIA Abstract Curretly there s creasgly dcated that most of classcal project maagemet methods s ot sutable

More information

Face Recognition using Supervised & Unsupervised Techniques

Face Recognition using Supervised & Unsupervised Techniques Natoal Uversty of Sgapore EE5907-Patter recogto-2 NAIONAL UNIVERSIY OF SINGAPORE EE5907 Patter Recogto Project Part-2 Face Recogto usg Supervsed & Usupervsed echques SUBMIED BY: SUDEN NAME: harapa Reddy

More information

Adaptive Clustering Algorithm for Mining Subspace Clusters in High-Dimensional Data Stream *

Adaptive Clustering Algorithm for Mining Subspace Clusters in High-Dimensional Data Stream * ISSN 673-948 CODEN JKYTA8 E-mal: fcst@vp.63.com Joural of Froters of Computer Scece ad Techology http://www.ceaj.org 673-948/200/04(09)-0859-06 Tel: +86-0-566056 DOI: 0.3778/j.ss.673-948.200.09.009 *,2,

More information

Cubic fuzzy H-ideals in BF-Algebras

Cubic fuzzy H-ideals in BF-Algebras OSR Joural of Mathematcs (OSR-JM) e-ssn: 78-578 p-ssn: 39-765X Volume ssue 5 Ver (Sep - Oct06) PP 9-96 wwwosrjouralsorg Cubc fuzzy H-deals F-lgebras Satyaarayaa Esraa Mohammed Waas ad U du Madhav 3 Departmet

More information

PRIVATE set intersection (PSI) is a cryptographic protocol that

PRIVATE set intersection (PSI) is a cryptographic protocol that Effcet Delegated Prvate Set Itersecto o Outsourced Prvate Datasets Ayd Abad, Sotros Terzs, Roberto Metere, Chagyu Dog Abstract Prvate set tersecto (PSI) s a essetal cryptographc protocol that has may real

More information

A PROCEDURE FOR SOLVING INTEGER BILEVEL LINEAR PROGRAMMING PROBLEMS

A PROCEDURE FOR SOLVING INTEGER BILEVEL LINEAR PROGRAMMING PROBLEMS ISSN: 39-8753 Iteratoal Joural of Iovatve Research Scece, Egeerg ad Techology A ISO 397: 7 Certfed Orgazato) Vol. 3, Issue, Jauary 4 A PROCEDURE FOR SOLVING INTEGER BILEVEL LINEAR PROGRAMMING PROBLEMS

More information

Fuzzy ID3 Decision Tree Approach for Network Reliability Estimation

Fuzzy ID3 Decision Tree Approach for Network Reliability Estimation IJCSI Iteratoal Joural of Computer Scece Issues, Vol. 9, Issue 1, o 1, Jauary 2012 ISS (Ole): 1694-0814 www.ijcsi.org 446 Fuzzy ID3 Decso Tree Approach for etwor Relablty Estmato A. Ashaumar Sgh 1, Momtaz

More information

Unsupervised Discretization Using Kernel Density Estimation

Unsupervised Discretization Using Kernel Density Estimation Usupervsed Dscretzato Usg Kerel Desty Estmato Maregle Bba, Floraa Esposto, Stefao Ferll, Ncola D Mauro, Teresa M.A Basle Departmet of Computer Scece, Uversty of Bar Va Oraboa 4, 7025 Bar, Italy {bba,esposto,ferll,dm,basle}@d.uba.t

More information

COMPARISON OF PARAMETERIZATION METHODS USED FOR B-SPLINE CURVE INTERPOLATION

COMPARISON OF PARAMETERIZATION METHODS USED FOR B-SPLINE CURVE INTERPOLATION Europea Joural of Techc COMPARISON OF PARAMETERIZATION METHODS USED FOR B-SPLINE CURVE INTERPOLATION Sıtı ÖZTÜRK, Cegz BALTA, Melh KUNCAN 2* Kocael Üverstes, Mühedsl Faültes, Eletro ve Haberleşme Mühedslğ

More information

Vanishing Point Detection: Representation Analysis and New Approaches

Vanishing Point Detection: Representation Analysis and New Approaches Publshed the Proceedgs of the th Iteratoal Coferece o Image Aalyss ad Processg (ICIAP ). IEEE. Persoal use of ths materal s permtted. However, permsso to reprt/republsh ths materal for advertsg or promotoal

More information

Proactive Detection of DDoS Attacks Utilizing k-nn Classifier in an Anti-DDos Framework

Proactive Detection of DDoS Attacks Utilizing k-nn Classifier in an Anti-DDos Framework World Academy of Scece, Egeerg ad Techology Iteratoal Joural of Computer ad Iformato Egeerg Proactve Detecto of DDoS Attacks Utlzg k-nn Classfer a At-DDos Framework Hoa-Vu Nguye ad Yogsu Cho Dgtal Ope

More information

An Enhanced Local Covering Approach for Minimization of Multiple-Valued Input Binary-Valued Output Functions

An Enhanced Local Covering Approach for Minimization of Multiple-Valued Input Binary-Valued Output Functions Proceedgs of the 10th WSEAS Iteratoal Coferece o COMPUTERS, Voulagme, Athes, Greece, July 13-15, 2006 (pp63-68) A Ehaced Local Coverg Approach for Mmzato of Multple-Valued Iput Bary-Valued Output Fuctos

More information

A modified Logic Scoring Preference method for dynamic Web services evaluation and selection

A modified Logic Scoring Preference method for dynamic Web services evaluation and selection A modfed Logc Scorg Preferece method for dyamc Web servces evaluato ad selecto Hog Qg Yu ad Herá Mola 2 Departmet of Computer Scece, Uversty of Lecester, UK hqy@mcs.le.ac.uk 2 Departmet of Iformatcs, School

More information

Associated Pagerank: A Content Relevance Weighted Pagerank Algorithm

Associated Pagerank: A Content Relevance Weighted Pagerank Algorithm Assocated Pagerak: A Cotet Relevace Weghted Pagerak Algorthm Jh-Shh Hsu,Cha-Che Ye Assocated Pagerak: A Cotet Relevace Weghted Pagerak Algorthm 1 Jh-Shh Hsu, 2 Cha-Che Ye 1 Natoal Yul Uversty of Scece

More information

A Model of Message-based Debugging Facilities for Web or Grid Services

A Model of Message-based Debugging Facilities for Web or Grid Services 2009 Cogress o Servces - I A Model of Message-based Debuggg Facltes for Web or Grd Servces Qag Yue, Xaoy Lu, Zhwe Xu, Haya Yu, L Zha Key Laboratory of Network Scece ad Techology Isttute of Computg Techology,

More information

International Mathematical Forum, 1, 2006, no. 31, ON JONES POLYNOMIALS OF GRAPHS OF TORUS KNOTS K (2, q ) Tamer UGUR, Abdullah KOPUZLU

International Mathematical Forum, 1, 2006, no. 31, ON JONES POLYNOMIALS OF GRAPHS OF TORUS KNOTS K (2, q ) Tamer UGUR, Abdullah KOPUZLU Iteratoal Mathematcal Forum,, 6, o., 57-54 ON JONES POLYNOMIALS OF RAPHS OF TORUS KNOTS K (, q ) Tamer UUR, Abdullah KOPUZLU Atatürk Uverst Scece Facult Dept. of. Math. 54 Erzurum, Turkey tugur@atau.edu.tr

More information

Network Security Evaluation Based on Variable Weight Fuzzy Cloud Model

Network Security Evaluation Based on Variable Weight Fuzzy Cloud Model 207 2 d Iteratoal Coferece o Computer Scece ad Techology (CST 207) ISBN: 978--60595-46-5 Networ Securty Evaluato Based o Varable Weght Fuzzy Cloud Model Yag JIANG a*, Cheg-ha LI, Zh-peg LI ad Mg-ca SUN

More information

Modeling and Analysis to Estimate the Performance of Heterogeneous Cluster

Modeling and Analysis to Estimate the Performance of Heterogeneous Cluster 50th Techcal evew of Isttuto of Egeers, Orssa State Cetre, pp. 3-8, 2009 Modelg ad Aalyss to Estmate the Performace of Heterogeeous Cluster Pushpedra Kumar Chadra, Bbhudatta Sahoo, Saay Kumar Jea Departmet

More information

Automated approach for the surface profile measurement of moving objects based on PSP

Automated approach for the surface profile measurement of moving objects based on PSP Uversty of Wollogog Research Ole Faculty of Egeerg ad Iformato Sceces - Papers: Part B Faculty of Egeerg ad Iformato Sceces 207 Automated approach for the surface profle measuremet of movg objects based

More information

For all questions, answer choice E) NOTA" means none of the above answers is correct. A) 50,500 B) 500,000 C) 500,500 D) 1,001,000 E) NOTA

For all questions, answer choice E) NOTA means none of the above answers is correct. A) 50,500 B) 500,000 C) 500,500 D) 1,001,000 E) NOTA For all questos, aswer choce " meas oe of the above aswers s correct.. What s the sum of the frst 000 postve tegers? A) 50,500 B) 500,000 C) 500,500 D),00,000. What s the sum of the tegers betwee 00 ad

More information

Speeding- Up Fractal Image Compression Using Entropy Technique

Speeding- Up Fractal Image Compression Using Entropy Technique Avalable Ole at www.jcsmc.com Iteratoal Joural of Computer Scece ad Moble Computg A Mothly Joural of Computer Scece ad Iformato Techology ISSN 2320 088X IMPACT FACTOR: 5.258 IJCSMC, Vol. 5, Issue. 4, Aprl

More information

The Similarity Measures and their Impact on OODB Fragmentation Using Hierarchical Clustering Algorithms

The Similarity Measures and their Impact on OODB Fragmentation Using Hierarchical Clustering Algorithms The Smlarty Measures ad ther Impact o OODB Fragmetato Usg Herarchcal Clusterg Algorthms ADRIAN SERGIU DARABANT, HOREA TODORAN, OCTAVIAN CRET, GEORGE CHIS Dept. of Computer Scece Babes Bolya Uversty, Techcal

More information

Region Matching by Optimal Fuzzy Dissimilarity

Region Matching by Optimal Fuzzy Dissimilarity Rego Matchg by Optmal Fuzzy Dssmlarty Zhaggu Zeg, Ala Fu ad Hog Ya School of Electrcal ad formato Egeerg The Uversty of Sydey Phoe: +6--935-6659 Fax: +6--935-3847 Emal: zzeg@ee.usyd.edu.au Abstract: Ths

More information

APPLICATION OF CLUSTERING METHODS IN BANK S PROPENSITY MODEL

APPLICATION OF CLUSTERING METHODS IN BANK S PROPENSITY MODEL APPLICATION OF CLUSTERING METHODS IN BANK S PROPENSITY MODEL Sergej Srota Haa Řezaková Abstract Bak s propesty models are beg developed for busess support. They should help to choose clets wth a hgher

More information

Deadlock Detection Based on the Parallel Graph Theory Algorithm

Deadlock Detection Based on the Parallel Graph Theory Algorithm Proceedgs of the d Iteratoal Coferece o Computer Scece ad Electrocs Egeerg (ICCSEE 013) Deadlock Detecto Based o the Parallel Graph Theory Algorthm Xaoru Wag, Qgxa Wag, Yudog Guo Cha Natoal Dgtal Swtchg

More information

An Ensemble Multi-Label Feature Selection Algorithm Based on Information Entropy

An Ensemble Multi-Label Feature Selection Algorithm Based on Information Entropy The Iteratoal Arab Joural of Iformato Techology, Vol., No. 4, July 204 379 A Esemble Mult-Label Feature Selecto Algorthm Based o Iformato Etropy Shg L, Zheha Zhag, ad Jaq Dua School of Computer Scece,

More information

Collaborative Filtering Support for Adaptive Hypermedia

Collaborative Filtering Support for Adaptive Hypermedia Collaboratve Flterg Support for Adaptve Hypermeda Mart Balík, Iva Jelíek Departmet of Computer Scece ad Egeerg, Faculty of Electrcal Egeerg Czech Techcal Uversty Karlovo áměstí 3, 35 Prague, Czech Republc

More information

CS 2710 Foundations of AI Lecture 22. Machine learning. Machine Learning

CS 2710 Foundations of AI Lecture 22. Machine learning. Machine Learning CS 7 Foudatos of AI Lecture Mache learg Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Mache Learg The feld of mache learg studes the desg of computer programs (agets) capable of learg from past eperece

More information

A Type of Variation of Hamilton Path Problem with Applications

A Type of Variation of Hamilton Path Problem with Applications Edth Cowa Uersty Research Ole ECU Publcatos Pre. 20 2008 A Type of Varato of Hamlto Path Problem wth Applcatos Jta Xao Edth Cowa Uersty Ju Wag Wezhou Uersty, Zhejag, Cha 0.09/ICYCS.2008.470 Ths artcle

More information

ANALYSIS OF VARIANCE WITH PARETO DATA

ANALYSIS OF VARIANCE WITH PARETO DATA Proceedgs of the th Aual Coferece of Asa Pacfc Decso Sceces Isttute Hog Kog, Jue -8, 006, pp. 599-609. ANALYSIS OF VARIANCE WITH PARETO DATA Lakhaa Watthaacheewakul Departmet of Mathematcs ad Statstcs,

More information

Office Hours. COS 341 Discrete Math. Office Hours. Homework 8. Currently, my office hours are on Friday, from 2:30 to 3:30.

Office Hours. COS 341 Discrete Math. Office Hours. Homework 8. Currently, my office hours are on Friday, from 2:30 to 3:30. Oce Hours Curretly, my oce hours are o Frday, rom :30 to 3:30. COS 31 Dscrete Math 1 Oce Hours Curretly, my oce hours are o Frday, rom :30 to 3:30. Nobody seems to care. Chage oce hours? Tuesday, 8 PM

More information

SVM Classification Method Based Marginal Points of Representative Sample Sets

SVM Classification Method Based Marginal Points of Representative Sample Sets P P College P P College P Iteratoal Joural of Iformato Techology Vol. No. 9 005 SVM Classfcato Method Based Margal Pots of Represetatve Sample Sets Wecag ZhaoP P, Guagrog JP P, Ru NaP P, ad Che FegP of

More information

Chapter 3 Descriptive Statistics Numerical Summaries

Chapter 3 Descriptive Statistics Numerical Summaries Secto 3.1 Chapter 3 Descrptve Statstcs umercal Summares Measures of Cetral Tedecy 1. Mea (Also called the Arthmetc Mea) The mea of a data set s the sum of the observatos dvded by the umber of observatos.

More information

Morphological Ending based Strategies of Unknown Word Estimation for Statistical POS Urdu Tagger

Morphological Ending based Strategies of Unknown Word Estimation for Statistical POS Urdu Tagger IJCSES Iteratoal Joural of Computer Sceces ad Egeerg Systems, Vol., No.3, July 2007 CSES Iteratoal c2007 ISSN 0973-4406 67 Morphologcal Edg based Strateges of Ukow Word Estmato for Statstcal POS Urdu Tagger

More information

Delay based Duplicate Transmission Avoid (DDA) Coordination Scheme for Opportunistic routing

Delay based Duplicate Transmission Avoid (DDA) Coordination Scheme for Opportunistic routing Delay based Duplcate Trasmsso Avod (DDA) Coordato Scheme for Opportustc routg Ng L, Studet Member IEEE, Jose-Fera Martez-Ortega, Vcete Heradez Daz Abstract-Sce the packet s trasmtted to a set of relayg

More information

Available online at ScienceDirect. Procedia Technology 16 (2014 )

Available online at   ScienceDirect. Procedia Technology 16 (2014 ) Avalable ole at www.scecedrect.com SceceDrect Proceda Techology 16 (2014 ) 128 137 CENTERIS 2014 - Coferece o ENTERprse Iformato Systems / ProjMAN 2014 - Iteratoal Coferece o Project MANagemet / HCIST

More information

A Web Mining Based Network Personalized Learning System Hua PANG1, a, Jian YU1, Long WANG2, b

A Web Mining Based Network Personalized Learning System Hua PANG1, a, Jian YU1, Long WANG2, b 3rd Iteratoal Coferece o Machery, Materals ad Iformato Techology Applcatos (ICMMITA 05) A Web Mg Based Network Persoalzed Learg System Hua PANG, a, Ja YU, Log WANG, b College of Educato Techology, Sheyag

More information

Fuzzy Dynamic Programming based Trusted Routing Decision in Mobile Ad Hoc Networks*

Fuzzy Dynamic Programming based Trusted Routing Decision in Mobile Ad Hoc Networks* Ffth IEEE Iteratoal Symposum o Embedded Computg Fuzzy yamc Programmg based Trusted Routg ecso oble Ad Hoc Networks* Zhwe Q, Zhpg Ja, Xhu Che School of Computer Scece ad Techology, Shadog Uversty, 25 Shadog,

More information

Multi Decryption Key Agreement in Dynamic Peer Group

Multi Decryption Key Agreement in Dynamic Peer Group 07 d Iteratoal oferece o omputer, etwork Securty ad ommucato Egeerg (SE 07) ISB: 978--60595-439-4 ult Decrypto Key Agreemet Dyamc Peer Group Ja ZHOU, ad L-ya SU School of aagemet Scece ad Egeer, Ahu Uversty

More information

A hybrid method using FAHP and TOPSIS for project selection Xuan Lia, Jiang Jiangb and Su Deng c

A hybrid method using FAHP and TOPSIS for project selection Xuan Lia, Jiang Jiangb and Su Deng c 5th Iteratoal Coferece o Computer Sceces ad Automato Egeerg (ICCSAE 205) A hybrd method usg FAHP ad TOPSIS for project selecto Xua La, Jag Jagb ad Su Deg c College of Iformato System ad Maagemet, Natoal

More information

Point Estimation-III: General Methods for Obtaining Estimators

Point Estimation-III: General Methods for Obtaining Estimators Pot Estmato-III: Geeral Methods for Obtag Estmators RECAP 0.-0.6 Data: Radom Sample from a Populato of terest o Real valued measuremets: o Assumpto (Hopefully Reasoable) o Model: Specfed Probablty Dstrbuto

More information

Greater Knowledge Extraction Based on Fuzzy Logic And GKPFCM Clustering Algorithm

Greater Knowledge Extraction Based on Fuzzy Logic And GKPFCM Clustering Algorithm 6th WSEAS It. Coferece o Computatoal Itellgece, Ma-Mache Systems ad Cyberetcs, Teerfe, Spa, December 14-16, 2007 47 Greater Kowledge Extracto Based o uzzy Logc Ad GKPCM Clusterg Algorthm BEJAMÍ OJEDA-MAGAÑA

More information

NUMERICAL INTEGRATION BY GENETIC ALGORITHMS. Vladimir Morozenko, Irina Pleshkova

NUMERICAL INTEGRATION BY GENETIC ALGORITHMS. Vladimir Morozenko, Irina Pleshkova 5 Iteratoal Joural Iformato Theores ad Applcatos, Vol., Number 3, 3 NUMERICAL INTEGRATION BY GENETIC ALGORITHMS Vladmr Morozeko, Ira Pleshkova Abstract: It s show that geetc algorthms ca be used successfully

More information

An Efficient Approach to Mining Frequent Itemsets on Data Streams

An Efficient Approach to Mining Frequent Itemsets on Data Streams A Effcet Approach to Mg Frequet Itemsets o Data Streams Sara Asar, ad Mohammad Had Sadredd Abstract The creasg mportace of data stream arsg a wde rage of advaced applcatos has led to the extesve study

More information

Personalized Search Based on Context-Centric Model

Personalized Search Based on Context-Centric Model JOURNAL OF NETWORKS, VOL. 8, NO. 7, JULY 13 1 Persoalzed Search Based o Cotext-Cetrc Model Mgyag Lu, Shufe Lu, Chaghog Hu Dept. College of Computer Scece ad Techology, Jl Uversty, Chagchu, Jl, Cha Emal:

More information

Properties of Linguistic 2-tuple Judgment Matrix with Additive Consistency

Properties of Linguistic 2-tuple Judgment Matrix with Additive Consistency Proertes of Lgustc -tule Judgmet Matrx wth Addtve Cosstecy Xxag Zhag Jg Le 3 Bao-a Yag Glorous Su School of Busess ad Maagemet Doghua Uversty Shagha 5 PRCha Iformato Egeerg School Jaxg College Jaxg 34

More information

Text Categorization Based on a Similarity Approach

Text Categorization Based on a Similarity Approach Text Categorzato Based o a Smlarty Approach Cha Yag Ju We School of Computer Scece & Egeerg, Uversty of Electroc Scece ad Techology of Cha, Chegdu 60054, P.R. Cha Abstract Text classfcato ca effcetly ehace

More information

Machine Learning. CS 2750 Machine Learning. Administration. Lecture 1. Milos Hauskrecht 5329 Sennott Square, x4-8845

Machine Learning. CS 2750 Machine Learning. Administration. Lecture 1. Milos Hauskrecht 5329 Sennott Square, x4-8845 CS 75 Mache Learg Lecture Mache Learg Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square, 5 people.cs.ptt.edu/~mlos/courses/cs75/ Admstrato Istructor: Prof. Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square,

More information

An Improved Fuzzy C-Means Clustering Algorithm Based on Potential Field

An Improved Fuzzy C-Means Clustering Algorithm Based on Potential Field 07 d Iteratoal Coferece o Advaces Maagemet Egeerg ad Iformato Techology (AMEIT 07) ISBN: 978--60595-457-8 A Improved Fuzzy C-Meas Clusterg Algorthm Based o Potetal Feld Yua-hag HAO, Zhu-chao YU *, X GAO

More information

Research Article Fault Diagnosis in Condition of Sample Type Incompleteness Using Support Vector Data Description

Research Article Fault Diagnosis in Condition of Sample Type Incompleteness Using Support Vector Data Description Mathematcal Problems Egeerg Volume 5, Artcle ID 465, pages http://dx.do.org/.55/5/465 Research Artcle Fault Dagoss Codto of Sample Type Icompleteess Usg Support Vector Data Descrpto Hu Y, Zehu Mao,, B

More information

Application of Genetic Algorithm for Computing a Global 3D Scene Exploration

Application of Genetic Algorithm for Computing a Global 3D Scene Exploration Joural of Software Egeerg ad Applcatos, 2011, 4, 253-258 do:10.4236/jsea.2011.44028 Publshed Ole Aprl 2011 (http://www.scrp.org/joural/jsea) 253 Applcato of Geetc Algorthm for Computg a Global 3D Scee

More information

AT MOST EDGE 3 - SUM CORDIAL LABELING FOR SOME GRAPHS THE STANDARD

AT MOST EDGE 3 - SUM CORDIAL LABELING FOR SOME GRAPHS THE STANDARD Iteratoal Joural o Research Egeerg ad Appled Sceces IJREAS) Avalable ole at http://euroasapub.org/ourals.php Vol. x Issue x, July 6, pp. 86~96 ISSNO): 49-395, ISSNP) : 349-655 Impact Factor: 6.573 Thomso

More information

AN IMPROVED TEXT CLASSIFICATION METHOD BASED ON GINI INDEX

AN IMPROVED TEXT CLASSIFICATION METHOD BASED ON GINI INDEX Joural of Theoretcal ad Appled Iformato Techology 30 th September 0. Vol. 43 No. 005-0 JATIT & LLS. All rghts reserved. ISSN: 99-8645 www.jatt.org E-ISSN: 87-395 AN IMPROVED TEXT CLASSIFICATION METHOD

More information

A New Newton s Method with Diagonal Jacobian Approximation for Systems of Nonlinear Equations

A New Newton s Method with Diagonal Jacobian Approximation for Systems of Nonlinear Equations Joural of Mathematcs ad Statstcs 6 (3): 46-5, ISSN 549-3644 Scece Publcatos A New Newto s Method wth Dagoal Jacoba Appromato for Systems of Nolear Equatos M.Y. Wazr, W.J. Leog, M.A. Hassa ad M. Mos Departmet

More information

Software reliability is defined as the probability of failure

Software reliability is defined as the probability of failure Evolutoary Regresso Predcto for Software Cumulatve Falure Modelg: a comparatve study M. Beaddy, M. Wakrm & S. Aljahdal 2 : Dept. of Math. & Ifo. Equpe MMS, Ib Zohr Uversty Morocco. beaddym@yahoo.fr 2:

More information

Using Linear-threshold Algorithms to Combine Multi-class Sub-experts

Using Linear-threshold Algorithms to Combine Multi-class Sub-experts Usg Lear-threshold Algorthms to Combe Mult-class Sub-experts Chrs Mesterharm MESTERHA@CS.RUTGERS.EDU Rutgers Computer Scece Departmet 110 Frelghuyse Road Pscataway, NJ 08854 USA Abstract We preset a ew

More information

A Disk-Based Join With Probabilistic Guarantees*

A Disk-Based Join With Probabilistic Guarantees* A Dsk-Based Jo Wth Probablstc Guaratees* Chrstopher Jermae, Al Dobra, Subramaa Arumugam, Shatau Josh, Abhjt Pol Computer ad Iformato Sceces ad Egeerg Departmet Uversty of Florda, Gaesvlle {cjerma, adobra,

More information

A New Hybrid Audio Classification Algorithm Based on SVM Weight Factor and Euclidean Distance

A New Hybrid Audio Classification Algorithm Based on SVM Weight Factor and Euclidean Distance Proceedgs of the 2007 WSEAS Iteratoal Coferece o Computer Egeerg ad Applcatos, Gold Coast, Australa, Jauary 7-9, 2007 52 A New Hybrd Audo Classfcato Algorthm Based o SVM Weght Factor ad Eucldea Dstace

More information

Laplacian Meshes Deformation Based on the Offset of Sketching

Laplacian Meshes Deformation Based on the Offset of Sketching JOURNAL OF SOFTWARE, VOL. 7, NO. 9, SEPTEMBER 202 2083 Laplaca Meshes Deformato Based o the Offset of Sketchg Sha Chemg School of Software, Harb Uversty of Scece ad Techology, Harb, Cha Emal: shachm@63.com

More information

Exploring Wireless Sensor Network Configurations for Prolonging Network Lifetime

Exploring Wireless Sensor Network Configurations for Prolonging Network Lifetime 60 IJCSNS Iteratoal Joural of Computer Scece ad Network Securty, VOL7 No8, August 007 Explorg Wreless Sesor Network Cofguratos for Prologg Network Lfetme Zh Zha ad Yuhua Che, Departmet of Electrcal ad

More information

Parallel Ant Colony for Nonlinear Function Optimization with Graphics Hardware Acceleration

Parallel Ant Colony for Nonlinear Function Optimization with Graphics Hardware Acceleration Proceedgs of the 009 IEEE Iteratoal Coferece o Systems Ma ad Cyberetcs Sa Atoo TX USA - October 009 Parallel At Coloy for Nolear Fucto Optmzato wth Graphcs Hardware Accelerato Wehag Zhu Departmet of Idustral

More information

DEEP (Displacement Estimation Error Back-Propagation) Method for Cascaded ViSPs (Visually Servoed Paired Structured Light Systems)

DEEP (Displacement Estimation Error Back-Propagation) Method for Cascaded ViSPs (Visually Servoed Paired Structured Light Systems) DEEP (Dsplacemet Estmato Error Back-Propagato) Method for Cascaded VSPs (Vsually Servoed Pared Structured Lght Systems) Haem Jeo 1), Jae-Uk Sh 2), Wachoel Myeog 3), Yougja Km 4), ad *Hyu Myug 5) 1), 3),

More information

Advanced mixing audio streams for heterogeneous computer systems in telecommunications

Advanced mixing audio streams for heterogeneous computer systems in telecommunications Advaced mxg audo streams for heterogeeous computer systems telecommucatos A.A. Kolpakov, Ju.A. Kropotov Murom sttute (brach) VlSU, Orlovskaya st., 3, 6064, Murom, Vladmr rego, Russa Abstract Ths paper

More information

Hybrid Landmark Routing in Ad Hoc Networks with Heterogeneous Group Mobility

Hybrid Landmark Routing in Ad Hoc Networks with Heterogeneous Group Mobility Hybrd Ladmark Routg Ad Hoc Networks wth Heterogeeous Group Moblty Yeg-Zhog Lee, Kax Xu 2, Xaoya Hog 3, Maro Gerla Computer Scece Departmet, Uversty of Calfora, Los Ageles, CA, {yeglee, gerla}@cs.ucla.edu

More information