Proceedings of the 21st lASTED International ConferenceAPPLIED INFORMATICSFebruary 10-13, 2003, Innsbruck, Austria

APPLICATION-ORIENTED SPATIAL GRAPH GRAMMARS, Jun KONG1 Kang ZHANG2 Maolin HUANG3 '\..-

1,2 Department of Computer Science, The University of Texas at Dallas USA3 Department of Computer Systems, University of Technology, Sydney, Australia

Abstract: The Reserved Graph Grammar (RGG) is ageneral graph grammar Jormalism that expresses a widerange oj visual languages, This paper presents anextension to RGG with the capability oj spatialspecification, Graph transJormation satisfying the spatialspecification can be performed in the process oj parsing.The RGG with spatial specification can be applied tovarious types oj applications, The paper demonstrates anexampleJor mathematical expression recognition.

Key Words: visual languages, spatial specifications,graph grammar, patter recognition

1. IntroductionMotivated by the theoretical support for patternrecognition, compiler construction, and data typedescription, graph grammars originated in the late 60shave stepped in a period with theoretically sound andwell-established foundation [1]. Graph grammars providea natural approach to describing complex structuresgraphically. Moreover, graph transformation can beviewed as a model to present a dynamical evolutionstarting from an initial graph. So, the number ofapplications interacting with graphs has increaseddramatically, There are already many well-known graphformalisms, such as Petri nets, Statecharts, flowcharts,and UML diagrams.

Compared to text, a graph can visually conveyrelationships and naturally represent structuralinformation. With well-established theoretical foundationgraph grammars are powerful syntax formalisms to definelogic relations among constructs of a graph. It, however,does not usually specify how those constructs look like, Inmany applications, spatial information plays an importantrole, For example, in graph layout, a graph grammarintroduces a formal framework to generate a syntax-directed layout, which captures the internal structure of agraph, In multimedia design, the approach based on graphgrammars provides the power of design validation andautomatic presentation. Also, graphs are intuitive topresent the logic and spatial relation among objects andgraph transformation techniques have been applied tosymbol recognition. We argue that a mechanism in thegrammar for the designer to explicitly specify the physicallayout for a graph apart from the internal structure isextremely useful for a wide range of applications.

Reserved Graph Grammar [2][3] is one of the context-sensitive graph grammar formalisms. Because of its

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context-sensitive with a parsing complexity of polynomialtime [4], RGGs can be applied to many applications,Though the RGG formalism provides a powerfulapproach to specifying internal structures, it ignores thespatial relations. This paper presents an extension toRGGs with the capability of spatial specification andapplies it to mathematical expression recognition,

Section 2 introduces graph transformation and ReservedGraph Grammars. Section 3 defines spatial relations,Section 4 gives an example. Section 5 discusses relatedwork, followed by the conclusion in Section 6.

2. Graph TransformationFor textual languages, there exist some general grammarformalisms such as context-free grammars and applicationtools such as YACC. Defining a general and expressivegraph grammar formalism with an efficient parsingalgorithm, however, has been an obstacle in theapplications of visual languages. Even for the mostrestricted classes of graph grammars, the membershipproblem in the parsing process is NP-hard [5].Consequently, a parser may not recognize a syntacticallycorrect graph or be inefficient in analyzing a large andcomplex graph.

Another obstacle in restnctmg applications of graphgrammars is that most proposed parsing algorithms[6][7][8] are context-free, while many interesting graphscannot be specified by pure context-free grammars.Additional control mechanisms are necessary to providecontext-sensitivity. It is, therefore, hard to apply context-free graph grammars to some applications, where thelogic structure of a graph is too complex to define withoutcontext information.

A graph grammar is made up of a set of rewriting rulescalled productions. One production consists of two sub-graphs, called left graph and right graph. A graphtransformation is a sequence of applications ofproductions. Applications are classified into L-applicationand R-application. An L-application/R-application is toreplace a sub-graph in the host graph, which is isomorphicto the left/right sub-graph of a production (commonlycalled match), with the right/left sub-graph of aproduction. In other words, rewriting a graph h into agraph h' is to replace a sub-graph m in h by another graphof d and to embed d into the remainder of h. Differentgraph grammars employ different embedding approaches

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[9]. The most difficult issue in graph transformations is todecide which matches are allowed and which are not.

The Reserved Graph Grammar (RGG) [3] is a context-sensitive graph grammar formalism with a parsingcomplexity of polynomial time. It provides a powerfulmechanism to represent structures graphically and toperform automatic syntax validation through theautomatically generated parser. Compared to context-freegrammars, a RGG is more intuitive in defining syntaxbecause of its context-sensitive property.

Since RGGs require a format that is suitable for thegrammar, a host graph is first translated into the format,called a node-edge form. The translation is quitestraightforward. Each node in the node-edge form isorganized into a two-level hierarchy. A large rectangle isthe first level called a super-vertex with embedded smallrectangles as the second level called vertices. Each vertexis labeled by a unique character.

RGG employs a marking technique to solve theembedding problem and to avoid ambiguities. If a super-vertex or a vertex is marked, it will reserve its outgoingedges connected to vertices outside the replaced sub-graph in the application of a production. On the contrary,if a super-vertex or a vertex is not marked, the replacedsub-graph must contain all of the edges connected to thisun-marked vertex. Otherwise, the so-called danglingcondition [10], which is not allowed in RGG, will occur.Therefore, the marking technique is useful indistinguishing the context and eliminating danglingconditions.

The process of parsing a graph can be viewed as: select aproduction from the grammar and apply a R-applicationto the host graph. This process is continued until noproduction can be applied. If the host graph is eventuallytransformed into an initial graph (i.e. A), the parsingprocess is successful and the host graph is considered tobelong to the language defmed by the graph grammar.One of the difficulties in the process of parsing is to selectan appropriate production when multiple choices exist,i.e. how to process ambiguities during parsing. Theselection outcome will affect the parsing efficiency andfinal result, since if the current path fails, we must trackback to test other paths, which costs computational time.RGGs use a deterministic parsing algorithm, calledselection-free parsing algorithm (SFPA), which only triesone parsing path [3]. Zhang et al. proves that the timecomplexity of SFPA is polynomial [4][11].

3. Spatial RelationsSpatial relations specify the geometrical relationshipsamong graph nodes. We classify the specifications intofour categories: Length, Direction, Alignment and Sizespecifications.

Length specification defines the distance among graphnodes. This type of specifications is introduced by the factthat if two nodes are close/remote semantically, theyshould be placed tightly/loosely in the graph. It consists ofthree relations: Close, Remote and Spring.

Direction specification expresses the directioninformation among nodes, such as left-to, above-of etc.Such information plays an important role in applicationssuch as graph layout, mathematical expressionrecognition etc. Direction relations are transitive. Forexample, if node A is left-to node B and node B is left-tonode C, we can induce that node A must be left-to node C.We can set up a partial order deduced from directionrelations among nodes. The partial order, through whichwe can distinguish two nodes' relative positions, providesa global view of a graph. Based on the partial order, wecan perform a spatial reasoning over the host graph.

Alignment specification addresses a special relationbetween two nodes along X-axis or Y-axis. Two nodeshold an alignment relationship if they are projected to X-axis or Y-axis, and the starting, ending or central points ofthe nodes are mapped to a same point on X-axis or Y-axis.

Size specification is used to indicate the size of a node,which can have practical meanings in applications. Forexample, we may use a node with large size to representlarge data processing capability. Size specifications aredefined by three measurements: large, medium and small.

3.1 Length SpecificationMotivated by the fact that the distance between two nodesin a graph can reflect their semantic relations, we definelength relations among nodes. For example, if two citiesare located closely, the line connecting the two cities in amap should be short.

• Close relationA close relation is represented by a line with an arrowlabeled "close". Semantically, close relation means thatthe nodes are highly related; in layout, it means that thenodes should be placed as tightly as possible.

• Remote RelationOn the contrary to the close relation, a remote relationrepresented by a line with an arrow labeled "remote",means that two nodes are loosely related semantically andplaced as far as possible when they are drawn.

• Spring RelationA spring relation represented by a line with an arrowlabeled "spring", indicates that the distance ranges fromclose to remote relation.

The above three relations as illustrated in the left portionof Figure 1 provide a way to specify the distancequalitatively. It, however, is not sufficient to describecomplex graphs, where we need to identify the variation

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of the spring relation. The above three relations will berefined by adding quantitative mechanism. We divide thedistance into 11 scales indexed from 0 to 10. A distanceindex will be assigned to a length relation. Instead ofreflecting the actual length to be displayed on the screen,the distance index provides a quantitative approach todefining length relation. Larger index denotes longerdistance. So, "1" is assigned to close relation and" 10" toremote relation. The spring relation can range from 2 to 9."0" indicates that two nodes are touched.

It is undesirable for the user to design the lengthspecifications without any system help. First, it is tediousto adjust the length manually; second, the length ofmanually produced lines cannot exactly reflect the scaleof the distance index. We will develop an authoring toolto ease the design task. The user only needs to choose thedistance index and the tool will automatically generate anappropriate line.

--------------------.Length specification: :

~<:;.IQ~~ :I

~_~e..lllo.!e..>1 NodeZJ:I

r.-;-;-;o Spring ~ I~----~~ :1- I

r-------------------: Direction specification:I

I: NW N NEI

: WeEII SW S SEI1 -----------------

Figure I Length specification and direction specification

3.2 Direction SpecificationWe divide a planar into nine districts. In order todetermine the direction relation between two nodes, onenode is chosen as the anchor node, which is placed in thecentral district. There are eight possible directions foranother node relative to the anchor node. Each of thosedirections is represented by a line labeled with N, W, S,E, NW, NE, SW or SE as illustrated in the right portion ofFigure 1. If a node falls into more than one district and weidentify its direction by the district where the main part ofthis node lies.

Figure 2 Sub-cases in NW direction

Obviously, the above eight direction relations are notsufficient to describe a complex graph. For example, iftwo nodes hold a NW relation, we need to differentiatethat one node leans to N or W direction. Figure 2 liststhree sub-cases of NW relation. The sub-cases for otherseven directions are similarly defined. A specification toolfor direction specifications can be easily implemented.The user first chooses one of eight directions, and threeicons representing three relative sub-cases will bedisplayed. When the user clicks one of the icons, the

system will automatically adjust the layout and generatedesirable relation.

3.3 Partial OrderDirection specification expresses the geometrical relationbetween two nodes, and is transitive among multiplenodes. For example, ifnI is left to n2 and n2 is left to n3,we can conclude that nl is left to n3. We, therefore, canderive a geometrical order, which achieves a global viewof relative positions among nodes in a graph.

• Partial order along Vertical DirectionWe define nt<ydireclionn2if and only ifn2 has a S, SE or SWrelation with n,. The relation POVD (partial order invertical direction) is defined as follows:POVD={(nJ,n2Y I ijn/<ydirectionn2} + {(n, n) I ne N}

• Partial order along Horizontal DirectionWe define nt< x directionn2if and only if nt has a W, NWorSW relation with n» The relation PORD (partial order inhorizontal direction) is defined as follows:POHD={(n/,n2Y I ijn/<xdirectionn2} + {(n,n) I ne N}

Four special elements are defined as follows based on thepartial order:Bottom: The maximal element in the (V,POVD) is calledbottom denoted by "1".Top: The minimal element in the (V,POVD) is called topdenoted by "T".Left: The minimal element in the (V,PORD) is called leftdenoted by "1-".Right: The maximal element in the (V,POHD) is calledright denoted by "-I".3.4 Alignment SpecificationIf two nodes are projected to X-axis or Y-axis, and thestarting, ending or central points of the nodes are mappedto the same point on X-axis or Y-axis, then we define thatthe two nodes have an alignment relation. Alignmentrelation along Y-axis is called horizontal alignment, andthat along X-axis is called vertical alignment.

• Horizontal AlignmentIn a graph, some nodes should be placed on a horizontalline. We define this relation as the horizontal alignmentrelation, represented by a line with an arrow labeled"HA". If two nodes with the HA relation is of the samesize, we can align either the upper-left point or the lower-left point. But if two nodes are of different sizes, we mustexplicitly specify how to align the two nodes. So, wedivide HA relation into three sub-cases as illustrated inFigure 3: bottom alignment denoted by HA-Bottom, topalignment denoted by HA-Top and central alignmentdenoted by HA-Cen.

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UO

de2HA-Bottom

I Nodel 1------HA-Cen ~~-----.L-JHA-Top

1Nodel I------UFigure 3 Horizontal alignment

• Vertical AlignmentVertical alignment is similar to horizontal alignmentexcept that it is along X-axis. Its three sub-cases aredenoted by Vn-Lefi, VA-Right and VA-Cen.

3.5 Size SpecificationIn a map, a large city is typically represented by a largecircle and a small city by a small one. So, the size of anode may convey useful information and we introduce thesize specification into the definition of the grammars. Thesize of one node can be classified into three categories:large, medium and small. A large node is represented by10*10 grids, and a small node by I *I grids. A mediumnode ranges from small size to large size.

4. Mathematical Expression RecognitionThe previous sections introduced the extended RGG withthe capability of spatial specifications. This sectionpresents the application of mathematical expressionrecognition to illustrate how to use the extended RGG.

In the electronic age, it is useful to convert paperdocument into an electronic form, which can be indexed,stored and retrieved in computers. Text can be recognizedby Optical Character Recognition (OCR) [12]. But, amore sophisticated mechanism is required to recognizehand-written mathematical expressions, which has beenresearched for several decades [12].

A pattern recognition problem is often solved in thefollowing three steps [13][14]. First, define a set ofprimitives and relations; second, recognize the primitivesand build up relations among primitives over an input;third, interpret the input according to the recognizedprimitives and relations. Mathematical expressionrecognition is one of the pattern recognition problems. Anapproach based on PROGRES has been used to themathematical expression recognition [14]. In this section,we will present how to recognize mathematicalexpressions based on the extended RGG.

+ / Letter Digit. (Point) FracLine Number

~I/~VOperator---- Operand----

TerminalFigure 4 The terminal node scheme

A handwritten or typed mathematical expression is firstscanned into an image. We use a node contained in abounding box to represent each mathematical symbol. In

order to illustrate the process, we use a simplifiedexpression. Totally, nine types of symbols organized in ahierarchy are defined in Figure 4. The root labeled asTerminal conveys general information, which can alsorepresent its child nodes. On the other hand, a leafconveys more specific information.

Generate a graphwith unnecessary Trimmed graph

...-_-,sp_a--.tialrelationsFirst phase Second Third

phase phaseSet ofnodes

Interpretation

Figure 5 Phases in mathematical expression recognition

We will perform a graph transformation over the set ofnodes through three phases as presented in Figure 5:constructing, rebuilding and parsing. In the first phase,four spatial relations among nodes will be built. Amathematical formula is often expressed in an irregularconvention. For example, numbers may not be writtenalong the same line, or not in the same size. Incorrectspatial relations may be introduced in the first phase. So,the host graph will be passed to the second phase toremove unnecessary spatial relations. At last, the trimmedgraph will be interpreted by a parser.

Constructing phase: In this phase, four types ofgeometrical relations, left, above, superscript andsubscript, are set up among nodes. Those four relationsare defined as follows:

• Left: One node is left to the other node.• Above: One node is above to the other node.• Superscript: One node is located North-East to

the other node.• Subscipt: One node is located South-East to the

other node.Figure 6 shows two of the productions in this phase.Through performing a calculation over the coordinates ofthe bounding box, we define several functions to test ifone of the four spatial relations holds between the nodes.

~ ~+~;'ij-~'HW-------------------------------------------Superscript h?d r~ ~-fu'~ .N . To .

Figure 6 Two productions in the constructing phaseIf the function proves true, a corresponding productionwill be applied to generate a spatial relation.

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Rebuilding phase: Since a mathematical expressioncan be written in an irregular way, spatial relations maybe built up incorrectly. Obviously, those errors will affectthe final interpretation. So the purpose of the secondphase is to eliminate noise-relations. For example, weneed to delete any spatial relation across a fractional line,and combine two digits and a point into a number. Thegrammar rules for this phase and next phase are omitteddue to the space limit.

Figure 7 A mathematical expression~~-------------::~~YJ 1: I FracLine ~:: \tV :

Ie: ~ :1_- .• 1 .•

r------------------------------c.

Figure 8 A recognition procedure

Parsing phase: When a graph is passed to the thirdphase, some unimportant and incorrect spatial relationsintroduced in the first phase have been eliminated. Aparser will perform a syntactic analysis and interpretation.

For example, if we need to recognize the mathematicalexpression: (ab+d)/e+c illustrated in Figure 7, Figure 8illustrates the recognition procedure. Graph A presents thegraph generated in the first phase. Obviously, someunnecessary spatial relations are created such as thespatial relation between a and e, and that between d and e.Graph B shows the result after eliminating noise relations.Graph C illustrates how to parse a graph passed from thesecond phase.

5. Related WorkGraphs are very suitable for describing objects with acomplex structure in a direct and intuitive way, so graphsare a hot research field in computer science. Originated in60s', graph grammars provide a formal approach tomodeling the evolution of static graphs by the applicationof productions. Though the research in graph grammarshas been conducted for decades, little work has been doneon developing graph grammars supporting spatialrelations explicitly. Drawing algorithms based on graphgrammars (DAGG) [15], such as the algorithm in [16],introduced a formal approach to graph layout. Its centralidea is to determine the node placement by an attributeevaluator. The spatial specification is addressed by theequation/in-equation between attributes which containsuch information as co-ordinates. Since spatial relationsare not represented visually, users may not catch thegeometrical specification at the first glance. In order tocomplete layout in a polynomial time, DAGG uses acontext-free graph grammar. Zhang et al. presented alayout algorithm based on RGGs [17]. This approach isused for graph layout. Its geometrical specification islimited to direction specification. We will compare theaforementioned approaches according to the followingcriteria as listed in Table 1:

1. Underlying graph grammar2. Applicationfield3. Complexity4. Spatial relation representation

Underlying graph Application field Spatial relation Complexitygrammar representation

DAGG [16] Context-free Layout Equation/in- Polynomialequation

Zhang et al. r 171 Context-sensitive Layout Graph PolvnomialOur approach Context-Sensitive General purpose Graph Polynomial

Table I Companson among graph grammars supporting spatial specification

6. Conclusion:Spatial information is useful in many applications. In thispaper, we have proposed to expand the RGG with thecapability of spatial specification. We applied this spatialgrammar to the applications of mathematical expressionrecognition.

U sing a graph grammar based approach, we can benefit inthe following aspects:

• Visual: Graph grammars provide a theoreticalfoundation for a visual approach to specifyingthe logic and spatial relations among theconstructs of a graph.

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• Automatic: A set of spatial constrains can beautomatically deducted in the process of parsingfrom the spatial specifications embedded in theproductions.

We plan our future work in the following two directions:• Enrich Spatial Relations: Spatial relations

extended in the RGG are far from complete. Forexample, topological relations are also animportant aspect in spatial relations.

• Embed Temporal Relations: In addition tospatial specifications among media objects,temporal specifications offer another importantdimension, which is useful in the design ofmultimedia documents.

References:[1] G. Rozenberg (ed.), Handbook on graph grammarsand computing by graph transformation: Foundations,VoU (World Scientific, 1997).

[2] D-Q. Zhang, and K. Zhang, Reserved graph grammar:A specification tool for diagrammatic VPLs, Proc. VL'97-13th IEEE Symposium on Visual Languages, Capri, Italy,1997,284-291.

[3] D-Q. Zhang, Generation of visual programminglanguages (Ph.D. Thesis, Macquarie Univ., 1997).

[4] D. Q. Zhang, K. Zhang, and J. Cao, A context-sensitive graph grammar formalism for the specificationof visual languages, The Computer Journal, 44(3), 2001,187-200.

[5] G. Rozenberg and E. Welzl, Boundary NLC graphgrammars - basic definitions, normal forms, andcomplexity, Information and Control, 69,1986,136-167.

[6] E. 1. Golin, A methodfor the specification and parsingof visual languages (ph.D. Thesis, Brown Univ., 1991).

[7] M. Kaul, Parsing of graphs in linear time, Proc. 2ndInt. Workshop on Graph Grammars and TheirApplication to Computer Science, LNCS 153, 1982, 206-218.

[8] L. M. Wills, Automated program recognition by graphparsing (Ph.D. Thesis, MIT AI Lab, 1992).

[9] A. Schurr, A.Winter, and A.Zundorf, Visualprogramming with graph rewriting systems, Proc. 11thIEEE Symposium on VL, 1995, 326 -333.

[10] J. Rekers, and A. Schurr, Defming and parsing visuallanguages with layered graph grammars, Journal of VisualLanguages and Computing, 8(1),1997,27-55.

[11] K. Zhang, D. Q. Zhang, and 1. Cao, Design,construction and application of a generic visual language

generation environment, IEEE Trans. SoftwareEngineering, 27(4),2001,289-307.

[12] D.Blosetin, and A.Grbavec, Recognition ofmathematical notation, Handbook of CharacterRecognition and Document Image Analysis, WorldScientific, 1997,557-582.

[13] K.S. Fu, Syntactic pattern recognition andapplications (Prentice Hall, 1982).

[14] D. Blosetin, and A. Schurr, Computing with graphsand graph transformation, Software - Practice andExperience, 29(3),1999,197-217.

[15] Y. Kahn, and T. Baylis, A survey of graph drawingalgorithm based on graph grammars, Proc. Fifth Inti.Workshop on Graph Grammars and Their Application toComputer Science, Williamsburg, Virginia, 1994.

[16] F. Brandenburg, Designing graph drawing by layoutgraph grammar, LNCS 894,1994,416-428.

[17] K. B. Zhang, K. Zhang, and M. A. Orgun, Grammar-based layout for a visual programming LanguageGeneration System, Proc. Diagram 2002, Georgia, USA,2002, 106-108.

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Publication Code: 378

Proceedings of the 21st lASTED International Conference on Applied Informatics, held February 10-13,2003, Innsbruck, Austria.

SponsorsThe International Association of Science and Technology for Development (lASTED)

• Technical Committee on Artificial Intelligence and Expert Systems• Technical Committee on Parallel and Distributed Computing and Systems• Technical Committee on Software Engineering• Technical Committee on Databases

EditorM.H.Hamza

International Program CommitteeE.M.A. Abed - Electronics Research Institute,

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PRCP. Chaudhuri - Univ. of the West Indies, Cave

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Continued on next page

Additional Reviewers - continued

I. Anagnostopoulos - GreeceF. Andre - FranceA. Andrzejak - USAC. Angeli - GreeceR. Anthony - UKB.O. Apduhan - JapanV.Ayala-Ramirez - MexicoF. Azuaje - UKY. Baghdadi - UAET. Baidyk - MexicoP. Bailes - AustraliaC. Bao - PRCB. Baran - ParaguayL. Bartek - Czech RepublicC. Benavente - SpainJ. Berger - CanadaI. Bluemke - PolandC. Bouras - GreeceP. Breznay - USAC.-A. Brunet - CanadaZ. Bubnicki - PolandZ.Q. Cai - SingaporeD. Camacho - SpainJ. Cangussu - USAZ. Car - CroatiaJ. Carmo - PortugalK. Cha - KoreaR. Chau - AustraliaY. Chen- PRCW.K. Cheuk - PRCL.K. Ching - USAC.-H. Chou - TaiwanY.-J. Chung - KoreaW. Chutimaskul - ThailandM. Ciglaric - SloveniaS. Corsava - UKX. Dai- PRCV. Dasigi - USAF. de Lange - The NetherlandsD. Delaney - IrelandS. Dembitz - CroatiaB.S. Dhillon - CanadaM.P. Diaz Perez - SpainR. Doallo Biempica - SpainW. Dosch - GermanyN. Drechsler - GermanyI. Dudnek - FranceR.-J. Dzeng - TaiwanR. Eggen - USAR. Erbacher - USAM. Eshtawie - LibyaD. Falcone - ItalyF. Famili - CanadaS. Fathy - EgyptI. Ferraz - BrazilA. Flores Mosri - UKY.H. Fu- PRCS. Fukumura - JapanJ.B. Galvan - SpainM. Garcia - USAA. Gelbukh - MexicoL. Gewali - USAV. Gianuzzi - ItalyF. Girault - France

R. Gitzel - GermanyC. Grelck - GermanyM. Grzenda - PolandZ.S. Guo - PRCM.S. Hamdi - UAEH. Harutyunyan - CanadaC. Heinlein - GermanyP. Heinzlreiter - AustriaM. Hericko - SloveniaK. Hettiarachchi - USAH. Higaki - JapanS. Hope - UKB. Horvat - SloveniaF.-H. Hou - TaiwanB. Hu - UKH. Hu- UKS. Huang - PRCY.-F. Huang - TaiwanE. Izquierdo - UKJ. Izy - IranC.K. Ji - PRCM. Jiang - PRCW.Jun-PRCS. Kalogeropoulos - GreeceY.-K. Kang - KoreaM. Karaata - KuwaitZ. M. Kasirun - MalaysiaR. Katarzyniak - PolandG. Kennedy - AustraliaM.~kuchi-JapanH.-G. Kim - KoreaP. S. Kim - KoreaS.D. Kim - KoreaS.H. Kim - KoreaA.C. Kochem - BrazilT. Koita - JapanK. Kono - JapanA. Kornecki - USAS. Kornienko - GermanyC. Kuhn - GermanyS. Kulikow - PolandO.Kum- USAS. Kuskabe - JapanS. Kwok-PRCM. Larrea - SpainM. Lazarescu - AustraliaC. Lee - TaiwanD.W. Lee - KoreaJ.I. Lee - KoreaR. Lencevicius - USAC. Li - PRCK.c. Li - USAY. Li - TaiwanX. Liang - USAB. Lilburne - AustraliaC.-G. Lim - KoreaH.Liu-PRCY. Liu - PRCF. Loulergue - FranceM. Lytras - GreeceS. Maad - GermanyD.A. Maluf - USAC. Mancas - RomaniaM. Marin - Chile

H. Masuyama - JapanH.C. Mayr - AustriaI. Miladinovic - AustriaR. Mitchell - UKR. Monroy - MexicoE. Monteiro - PortugalS. Montero - SpainI. Moughrabi - LebanonM. Mouhoub - CanadaM. Mounir - TunisiaA. Movaghar - IranA. Munitic - CroatiaR. Murillo Garcia - UKI. Nagasaka - JapanA. Navarra - ItalyE.J. Nemer - USAJ.J. Neto - BrazilT. Niculiu - Romaniac.F. Nourani - USAF. O'Brien - AustraliaS. Ono - JapanM. Ostojic - CanadaT. Ould Braham - FranceM. Ould-Khaoua - UKG. Pallis - GreeceA. Paradkar - USAG. Park - KoreaJ.H. Park - USAT.J. Park - KoreaC. Peng - FinlandG. Perichinsky - ArgentinaT. Peschel-Findeisen - GermanyJ. Polpinij - ThailandV. Ponomaryov - MexicoA. Prodan - RomaniaJ. Pu - CanadaL. Pyeatt - USAT. Rabie - CanadaW. Radinger - AustriaS. Radu - RomaniaH. Ramampiaro - NorwayH. Ren-Junn - TaiwanP. Robertson - USAD. Roy - IndiaV. Ryabov - FinlandY.-S. Ryu - KoreaS. Sadaoui - CanadaG. Sakarian - GermanyT. Sakata - BrazilS. Sakurai - JapanM.O. Saliu - Saudi ArabiaA.A. Salman - KuwaitR. Salomon - GermanyM. Sanchez - SpainP. Sapiecha - PolandP. Schlechtl - AustriaV. Sebesta - Czech RepublicY.-Y. Seow - SingaporeM. Serge - FranceP. Seung-Byun - KoreaS. Shelford - CanadaY. Shen - USAL.B. Sheremetov - MexicoK. Shin do - Japan

M. Sigmund - Czech RepublicV. Sikka - USAA.R. Silva - PortugalG. Singh - IndiaP.S. Singh - IndiaK. Sivalingam - USAV. Sivaraman - IndiaF. Smain - FranceG. Soldar - UKC.-F. Sorensen - NorwayG. Spezzano - ItalyK. Starkov - MexicoJ.P. Stitt - USAM. Strefezza - VenezuelaG. Stuer - BelgiumR.K. Subramanian - MalaysiaP. Sung Ho - KoreaL. Sung-II - KoreaM. Szychowiak - PolandN. Tagoug - UAEI. Taha - EgyptT.Takahashi-JapanH. Takeda - JapanH. Takizawa - JapanJ. Talaq - BahrainS. Tang- PRCY. Tang- PRCA.-A. Tarek - FranceW. Tarng - TaiwanCi-S, Tau - TaiwanJ. Teuhola - FinlandR. Thulasiram - CanadaT. Torabi - AustraliaM. Torres - MexicoM. Tounsi - FranceE. Tovar - SpainI. Traore - CanadaA.K. Turuk - IndiaB. Ustundag - TurkeyJ. Valls - SpainS. Vilapakkam Nagarajan -AustraliaJ. Vincent - UKK. Votis - GreeceX. Wang - CanadaS. Williams - UKM.Y.L. Wong - PRCM.-Y. Wu - USAC.-W. Xu - USAP.Xu - PRCI.E. Yairi - JapanT. Yairi - JapanS. Yang - PRCY.Yu- PRCY.Y. Yu - BelgiumS. Zein-Sabatto - USAK. Zhang - USAX. Zhao - PRCY. Zhao - CanadaT. Zhe - SingaporeX. Zou - USA

TABLE OF CONTENTS

COVER PAGE

INTERNATIONAL PROGRAM COMMITTEE

ADDITIONAL REVIEWERS

PUBLICA TION INFORMATION

I. ARTIFICIAL INTELLIGENCE ANDAPPLICATIONS (AlA)

Machine Learning and Data Mining

An Immune-inspired Approach to Learning andClassificationF. Azuaje J

Learning Topological Structures ofPOMDP-basedState Transition Models by State Splitting MethodT Yairi, M Togami, and K Hori 7

Acquisition of a Concept Relation Dictionary forClassifying E-mailsS Sakurai, A. Suyama, and K Fume J 3

A Formal Analysis of Classifier System and Interfacebetween Learning System and EnvironmentI. Nagasaka, M Kikuchi, and S Kitamura 20

Reinforcement Learning with Decision TreesL.D. Pyeatt 26

Data Mining: Understanding Data and DiseaseModelingA. Fazel Famili and J. Ouyang 32

The Data Mining Techniques in the Macroeconomic FieldL. Bordoni and S. Spadaro 38

Intelligent Databases

Concept-based Search System using Context-conservingExtension based on Conceptual GraphH.-K Bae, S-J. Park, and K-T. Kim .44

SP ARROW: A Spatial Clustering Algorithm usingSwarm IntelligenceG. Folino and G. Spezzano 50

EOM Layered Implementation of Life CycleU.-D. Choi and J.-H. Park 56

Towards Modelling an Intelligent Calendar Agentwith LUPSJ.C. Acosta Guadarrama and MJ. Osorio Galindo 60

Intelligent Concurrent SystemsF. Akkawi, A. Bader, and T. Elrad 66

Diagnosing Faulty Situations during AllianceFormation ProcessV. Mashkov and V.Marik 72

A General Framework for the Transformation ofStructured Data into Vector RepresentationsR. C. Mintram and J. Vincent 79

Genetic Algorithms

The Force Model: Concept, Behavior, InterpretationR. Salomon 85

On the Scalability of a Parallel Genetic Algorithmbased on Domain DecompositionJ. Vincent and R. Mintram 9J

A Multiobjective Ant Colony System for VehicleRouting Problem with Time WindowsB. Baran and M Schaerer 97

A Novel Approach for Parameter Extraction andCharacteristics Simulation of Deep-SubmicronMOSFET's with a Genetic AlgorithmY. Li and Y.-Y. Cho 103

Minimization of Transitions by Complementationand Resequencing using Evolutionary AlgorithmsR. Drechsler and N. Drechsler 109

Modelling of Explosive Welding Process usingGMDH-type Neural Networks and Genetic AlgorithmsN. Nariman-zadeh, A. Darvizeh, andG.R. Ahmad-zadeh J J 5

Artificial Intelligence and Applications I

Impact on Information Technology Usage andOrganizational Performance in Taiwanese BusinessOrganizationsF.-H. Hou 121

An Adaptive Color Segmentation Algorithm forSony Legged RobotsB. Li, H. Hu, and 1. Spacek 126

An Agent Architecture for Information Fusion andits Application to Robust Face IdentificationP. Robertson and R. Laddaga 132

A Decision of Agent-based Interface Among NursingSupport ServicesT. Takahashi, Y. Nagasaka, T. Takeshita, T. Aoki,and H. Fudaba 140

Developing a Personal Multilingual Web SpaceR. Chau, Ci-H. Yeh, and KA. Smith 145

XML Technology for the Diffusion of the Awarenessof Water ResourcesA. Colagrossi and A. Scaringella 151

Flexible Manufacturing Process Planning based on theMulti-agent TechnologyS. Kornienko, 0. Kornienko, and P. Levi 156

Improvements to a Dialogue Interface for a LibrarySystem1. Bartek 162

Intention Recognition for Vehicle Driving by Sensingof User and EnvironmentI.E. Yairi, T. Yairi, and S. Igi 166

Large-scale Ubiquitous Information System for DigitalMuseumK Shindo, N. Koshizuka, and K Sakamura 172

Seismic Signal Analysis using Correlation DimensionS. Ezekiel, MJ. Barrick, and M Lang 179

Artificial Intelligence and Applications II

Visually Augmented POMDP for Indoor RobotNavigationME. Lopez, R. Barea, L.M Bergasa, andMS. Escudero 183

SRAKA: A Case of Web Portal Architecture Centeredaround Horizontal ServicesB. Horvat, M Ojstersek, and Z. Cajic 188

An Intelligent Architecture for Mobile DigitalTelevision ApplicationsC. Peng and P. Vuorimaa 194

Segmentation of Thai Handwritten Word using HeuristicMethod based on Distinctive FeaturesK Kiratiratanaphrug, S. Kunaruttanapruk,R. Budsayaplakorn, and S. Jitapunkul 200

Classifying Glaucomatous Progression usingDecision TreesM. Lazarescu and A. Turpin 205

Application-oriented Spatial Graph GrammarsJ. Kong, K Zhang, and M. Huang 21 0

Speaker-specific Long-time SpectrumM Sigmund and T. Dostal 216

Medical Image Segmentation using MultifractalAnalysisS. Ezekiel 220

Forecasting Electricity Demand using ClusteringR.Mitchell 225

Facial Features Tracking Applied to Drivers DrowsinessDetectionL.M. Bergasa, R. Barea, E. Lopez, M. Escudero,1. Boquete, and J.I. Pinedo 231

Neural Networks

An Artificial Neural Network Approach for ClassifyingE-Commerce Web PagesI. Anagnostopoulos, G. Kouzas, C. Anagnostopoulos,I. Psoroulas, D. Vergados, V. Loumos, andE. Kayafas 237

Image Recognition System for Microdevice AssemblyT. Baidyk, E. Kussul, and 0. Makeyev 243

Forward Integrated Feature and Architecture SelectionAlgorithm using Neural NetworksE. Dawit and T. Sortrakul 249

A Comparison Study of Vector QuantizationCodebook Design Algorithms based on theEquidistortion PrincipleH. Takizawa, T. Nakajima, K Sano, andH. Kobayash 255

A Decision Criterion to Relocate Codewords forAdaptive Vector QuantizationH. Takizawa 262

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Comparison of Training Algorithms for Optimal NeuralControllers1.B. Galvan 269

How the Selection of Training Patterns can Improvethe Generalization Capability in Radial Basis NeuralNetworksJ.M Valls, I.M Galvan, and P. Isasi 275

Modified Self-organizing Maps for Line Extraction inDigitized Text DocumentsJ.M Alonso-Weber, I.M Galvan, and A. Sanchis 281

Identification of Handwritten DigitsMH. Ahmad Fadzil and A.M Intan Mastura 287

Fuzzy Visual Servoing for an Active CameraA. Perez-Garcia, V.Ayala-Ramirez, andR. Jaime-Rivas 292

Diagnostic and Learning Interaction System forHistorical Text ComprehensionG. Tsaganou, M. Grigoriadou, and T Cavoura 297

Knowledge Acquisition, Representation, andReasoning

Semi-Automatic Parsing for Web KnowledgeExtractionD. Camacho, MA. Lopez, and R. Aler 303

Knowledge Acquisition and Energy: A Case StudyS Dembitz, P. Knezevic, and M Sokele 309

Robotic Societies: Elements of Learning by ImitationC.A. Acosta Calderon and H. Hu 315

Representation of Multimedia Concepts and Objectsin a Virtual Education EnvironmentM.M.F. Yusofand E.-T Yeoh 321

Infusion: A Hybrid Reasoning System withDescription LogicsB. Hu, E. Compatangelo, and I. Arana 327

Linguistic Modifiers and Approximate ReasoningMEl-Sayed and D. Pacholczyk 333

Knowledge-based System for Supervision and Control ofa Fed-batch Fermentation of BTL. Valdez-Castro, A. Loza-Vdzquez,J.M Rodriguez-Morales, and J. Barrera-Cortes 339

Developing the User Interface for an Online Knowledge-based SystemC. Angeli, L. Vrizidis, and A. Chatzinikolaou 345

Industrial Diagnostics using Algebra of UncertainTemporal RelationsV. Ryabov and V. Terziyan 351

On the Three Forms of Non-deductive Inferences:Induction, Abduction, and DesignM Kikuchi and I. Nagasaka 357

A Rearrangement System for Floor Layouts based onCase-based Reasoning and Constraint SatisfactionS Dna, Y Hamada, K. Mizuno, Y Fukui, andS. Nishihara 363

Natural Language Processing

A Polish Document SummarizerN. Suszczanska and S Kulikow 369

Treating "Hata"- Construction in Korean-Chinese MTY-A. Sea, Y Huang, M Hong, and S-K. Choi 375

A Natural Language Interface to a B2C SystemV.Loerch and M. McKessar 381

Grounding Atom Formulas and Simple Modalities inCommunicative AgentsR. Katarzyniak 388

Planning and Scheduling

Dynamic Path Consistency for Interval-based TemporalReasoningM Mouhoub 393

Experimenting the Performance of AbstractionMechanisms Through a Parametric HierarchicalPlannerG. Armano, G. Cherchi, and E. Vargiu 399

AP-[RMA: Planning with Intentional AttentionToward ChancesCK Ji 405

Approximation Algorithm for Multi-Facility LocationL.P. Gewali, P. Kodela, and J. Bhadury 411

II. PARALLEL AND DISTRIBUTEDCOMPUTING AND NETWORKS (PDCN)

Architecture, Algorithms, Programming, andApplications

The J2EE-Adapted TINA Model: IntegratingTINA Solutions to InternetE. Yanaga, L. Nacamura Jr., and E.L. Bodanese 417

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Dynamic Value Prediction using Loop and LocalityPropertiesP.-J. Chuang, Y-T Hsiao, and y-s. Chiu.. ...423

Multicast Algorithms in Torus NetworkHA. Harutyunyan and X Liu 429

Detection of Generalized Conjunctive Predicatesfor Debugging Parallel Distributed ProgramsC. G. Ribeiro, L.M.A. Drummond, andR.S. Wedemann 435

An Experimental Result for Broadcast TimeHA. Harutyunyan and B. Shao .441

AN SNMP Enabled System for Enhanced MonitoringofE-commerce Statistical IndicatorsT Kotsilieris, J. Anagnostopoulos, S. Kalogeropoulos,A. Michalas, V.Loumos, and E. Kayafas 446

A Java Reliable MultiPeer Protocol for DistributedVirtual EnvironmentsG. Stuer, J. Broeckhove, and F. Arickx 450

Parallel Bidirectional Search on Message PassingEnvironmentK. Cha, J. Hong, and 0. Byeon 456

Functional Bulk Synchronous Parallel Programming inC++F. Dabrowski and F. Loulergue 462

A Fair and Starvation Free Version of Lamport's FastMutual Exclusion AlgorithmS. Vijayaraghavan 468

Loop Shifting Conversion for the ParallelizingCompilerK. 1wasawa and T Kusaba 472

A Multithreaded Compiler Backend for High-levelArray ProgrammingC. Grelck 478

A Proposed Methodology to Improve the Performanceof Agent-based Applications through the Identificationof the Optimum Visited SequenceS. Kalogeropoulos, T Kotsilieris, Moshe Sidi,H Gazit, and J. Tuch 485

Fast Recursive Data Processing in Graphs usingReductionJ.H ter Bekke and J.A. Bakker 490

Cluster and Heterogeneous Computing

Hardware Impact on Communication Performance ofBeowulfLINUX ClusterY Tang, Y-Q. Zhang, J.-c. Sun, and y-c. Li .495

Intelligent Fault Tolerant Architecture for ClusterComputing: A High Level OverviewS. Corsava and V. Getov 501

LSWS: A Linux-based Secure Web Switchc.t: Chow, G. Godavari, and Y Cai 507

Enhance Features and Performance of a Linux-basedContent SwitchC.E. Chow and C. Prakash 513

A Transparent Communication Layer for Heterogenous,Distributed SystemsT Fuerle and E. Schikuta 519

Implementation and Evaluation of Resource Allocationfor a Genomic Application Program on the GridT Koita, Y Kofune, Y Inoue, and A. Fukuda 524

Equivalent Query Transformer for Semantic QueryOptimization in Distributed Computing EnvironmentN. Pandey and G.K. Sharma 529

Task Scheduling for Jobs of a Non-constant Workloadover a Heterogeneous NetworkC. Maple, Y Wang, and J. Zhang 535

Web Delay Analysis and Reduction by Use of LoadBalancing ofa Dispatcher-based Web Server Clustery-w. Bai and y-c. Wu 541

Building Clusters on Modern Desktop Operating SystemsS. Juhasz and H Chara 547

Mobile Computing and Networks I

Software Testing for Ubiquitous Computing DevicesI. Satoh 553

Multimedia Systems Adaptation in MobileEnvironmentsF. Andre and B. Deniaud 559

Distributed Optimal Admission Controllers for ServiceLevel Agreements in Interconnected NetworksS. Shelford, M.M. Akbar, E.G. Manning, andG.c. Shoja 565

An Ethernet based Metropolitan Area Open AccessArchitectureN. Mors, A. Pettersson, and M. Jonsson 571

A Performance Evaluation of the Mobile AgentTechnology in Comparison to the Client/ServerParadigm for QoS Configuration in DiffServDomainsS. Kalogeropoulos, T Kotsilieris, G. Karetsos,A. Michalas, V. Loumos, and E. Kayafas 577

iv

CAC, Resource Reservation and Scheduling for QoSProvisioning in GPRSA. C. Barreiras Kochem, E.L. Bodanese, andL. Nacamura, Jr 583

Hierarchical HandoffMechanism based on SituationLearningT.J. Park and S.M. Park 589

Traffic Modeling and Simulation based PerformanceAnalysis of Mobile Multimedia Data ServicesY-1. Chung, C.-H Paik, and H-G. Kim 596

PRAM Simulation in Hierarchical InterconnectionNetworks through Hypercube EmbeddingP. T. Breznay 602

Congestion Control for High Performance VirtualCut-through NetworksL. Fernandez and 1.M. Garcia 608

Mobile Computing and Networks II

Comer-First Tree-based Region Broadcasting inMesh NetworksH Haddad and M. Mudawwar 615

Key Issues in Implementing an Optoelectronic PlanarFree-space Architecture for Signal ProcessingApplicationsH Forsberg, M. Jonsson, and B. Svensson 621

SLA Admission Controller for Reliable MPLSNetworks1. Pu, M. Akbar, E. Gowland, G. C. Shoja, andE. Manning 630

The Utilization of Distributed Processing on SignalingCalls in High Speed ATM Network: Architecture,Modeling, Simulation & Performance AnalysisR. Citro and S. Ghosh 636

Jitter Equalization to Maintain QoS for MultimediaTrafficH Elsayed and T. Saadawi 643

How to Evaluate the Quality of Service of Satellitebased Content Delivery NetworksH Hlavacs and G. Aschenbrenner 649

Wrapped System-Call: Cooperating Interactions betweenUser and Kernel Mode in an Operating System forFine-grain Multi-threadingS. Kusakabe, Y. Nomura, H. Taniguchi, andM. Amamiya 656

Preemptive Resource Management: Defending againstResource Monopolizing DoSW. Kaneko, K. Kono, and K. Shimizu 662

On the Choice of Checkpoint Interval1. Hong, S. Kim, and Y Cho 670

Applying Active Networks to Networked ControlSystemsX Zheng, C. Jin, and 1. Ju 676

Routing and Scheduling

Hierarchical Load-balanced Routing Via BoundedRandomization based on Destination NodeS. Bak and S.-Y Kang 681

On Behavior of Fuzzy Norms and F LearningAutomata in Distributed Multicriteria NetworkRoutingK. Lukac, Z. Lukac and M. Tkalic 686

Optimum Interval Routing in k-Caterpillars andMaximal Outer Planar NetworksG.s. Adhar 692

A Control Interface for Router ExtensionM. Alutoin and P. Raatikainen 697

A New Algorithm for the Ring Loading Problem withDemand SplittingY.-s. Myung and H.-G. Kim 703

A Heuristic Algorithm for the Static Scheduling onMultiprocessors1. Brest and V. Zumer 707

Scheduling Nested Loops with the Least Number ofProcessorsT. Andronikos, M. Kalathas, FM. Ciorba,P. Theodoropoulos, G. Papakonstantinou, andP. Tsanakas 713

Parallel Processing and Computing

Debugging Distributed Computations by ReverseSearchA. Andrzejak and K. Fukuda 719

SPaM - A Simple Parallel Machine in CORBAc.-w. Xu and W. Lee 726

Implementation and Evaluation ofMPI+OpenMPProgramming Model on Dawning3000Y. Chen, G. Chen, Y. Xu, and J. Shan 732

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Performance Evaluation ofa Prototype ofRHiNET-2:A Network-based Distributed Parallel ComputingSystemT. Otsuka, K Watanabe, J. Tsuchiya, H Harada,J. Yamamoto, H Nishi, T. Kudoh, and H Amano 738

Normal Versus Worst-case Performance in HighAvailability Cluster and Distributed ComputingL. Lundberg and C Svahnberg 744

Comparing the Optimal Performance of MultiprocessorArchitecturesL. Lundberg, K Klonowska, M Broberg, andH Lennerstad 750

Javaspaces Versus Remote Method Invocation:What is the Cost?R. Eggen and M Eggen 761

DSP-PP: A SimulatorlEstimator of Power Consumptionand Performance for Parallel DSP ArchitecturesD.Q. Minh, L. Bengtsson, and P. Larsson-Edefors 767

Web Technologies and Protocols

Managing Users and Services using an LDAP basedWeb-applicationC Bouras, V'-J. Khan, A. Limperis, andC Sintoris 773

A Study on Workload Characterization for a WebProxy ServerG. Pallis, A. Vakali, L. Angelis, and MS. Hacid 779

Two-level Scheduling of Discrete-event SimulationServersM Marin and C Bonacic 785

Topology Evolution in P2P Distributed NetworksG. Sakarian and HUnger 791

An Authentic Key Agreement Protocol based on theWeil PairingC Popescu and I. Mang 797

Multi-recipient Requests in the Session InitiationProtocolI. Miladinovic and J. Stadler 801

ECN is Fine - But Will it be Used?M Malowidzki 807

Message Routing in Pure Peer-to-Peer NetworksM Ciglaric, M. Trampus, M. Pancur, andT. Vidmar 813

Distributed Systems

Modeling and Real-time Simulation of Complex MediaProcessing Systems with Parallel DistributedComputingA.A.J. de Lange and I.C Kang 818

A Pipe lined Ring Network - Heterogeneous Real-time inRadar Signal ProcessingC Bergenhem and M Jonsson 825

On Reducing Entity State Update Packets in DistributedInteractive Simulations using a Hybrid ModelD. Delaney, T Ward, and S. McLoone 833

Distributed Data Management for Moving ObjectsR. Xie and R. Shibasaki 839

A Generated Management for Distributed SystemsT. Peschel-Findeisen and B. Hiitter 845

Distributed Video Editing for Array of ProjectorsH Takeda, T Moriya, K Utsugi, Y Sato,M Yamasaki, and T. Minakawa 851

Fault Tolerance

Replication of Checkpoints in Recoverable DSMSystemsJ. Brzezinski and M Szychowiak 857

On the Weakest Failure Detector for Hard AgreementProblemsM Larrea 863

Super-adaptive StabilizationMH Karaata 869

A Robust Tri-state Election Algorithm for ScalableApplicationsR.J. Anthony 875

Multimedia Checkpoint Protocol with LowerRecovery OverheadS. Osada and H Higaki 881

An Alternative Decoding Algorithm for EVENODDCode in Raid ArchitecturesC-S Tau and T.-I. Wang 887

A Recovery Technique of Agents in Multi-agent basedFault-tolerant SystemsH-M Lee, KS Chung, S-C Shin, Y-J. Lee,T-M Yoon, w.-G. Lee, and H-C Yu 893

vi

Ill. SOFTWARE ENGINEERING (SE)

Software Architecture, Programming Languages, andEmbedded Systems

An Adaptive Architecture for Presenting InteractiveMedia Onto Distributed Interfaces1. Hu and L. Feijs 899

Extracting High-level Architecture from Existing Codewith Summary ModelsN. Mansurov and D. Campara 905

Water Resources Modelling and Simulation Software:An Integrated ApproachF. El Dabaghi, D. Ouazar, and N. Souissi 913

Why Functional Programming Really MattersP.A. Bailes, C1.M. Kemp, JD. Peake, andS Seefried 919

Incremental Enhancement of the Expressivenessof a Reengineering Tool Development PlatformP.A. Bailes and J.D. Peake 927

Service-based Development of DynamicallyReconfiguring Embedded SystemsM Trapp, B. Schiirmann, and T. Tetteroo 935

Experimental Software Schedulability Estimationfor Varied Processor FrequenciesS Fabritius, R. Lencevicius, E. Metz, andA.Ran 942

XML Schema for Software Process FrameworkT. Torabi, T. Dillon, and W. Rahayu 948

Applying Practices of Extreme ProgrammingH. Hericko, MB. Juric, M Rostaher, andP. Repine 955

Automated Generation of Code ComplianceCheckersSK. Aggarwa and, R.S V. Raghavan 961

Software Design, Specifications, Development, andReusability

Developing Pattern Implementation Knowledge forReinforcing Software Design PatternsY Zhao 967

Specification Support to the Synergy of CompoundDesign Patternsl.K.-H. Mak, CS-T. Choy, and D.P.K. Lun 973

Comparison of Frameworks and Tools for Test-drivenDevelopmentM Pancur, M Ciglaric, M. Trampus, andT. Vidmar · 980

Function Points in Object Oriented Analysis and DesignA. Zivkovic, M Hericko, and J. Rozman 986

C-QM: A Practical Quality Model for Evaluating COTSComponentsSD. Kim and 1.H. Park 991

Software Testing and Security

Attacking End Users' Applications by Run TimeModificationsM Trampus, M Ciglaric, M. Pancur, andT. Vidmar 997

Enhanced Authentication Key Agreement ProtocolR.-1. Hwang, S-H. Shiau, and C-H. Lai 1003

Design of an Autonomous Anti-DDoS (A2D2)NetworkA. Cearns and CE. Chow 1007

Selecting Small Yet Effective Set of Test DataA. Paradkar 1013

On Automating the Formulation of Security Goalsunder the Inductive ApproachR. Monroy and M Carrillo 1020

Web-based Systems and Applications

A Web-based Review System and its Usability ina Small Software OrganizationE. Yanbas and 0. Demirors 1026

Experience Paper: Migration of a Web-based Systemto a Mobile Work EnvironmentC-F. Serensen, A.I. Wang, and 0. Hoftun 1033

A Comparison of Two Different Java Technologiesto Implement a Mobile Agent SystemA./. Wang, C-F. Sorensen 1039

A DTD Complexity MetricR. McFadyen and Y. Chen 1045

A Framework for the Analysis and Comparison ofHypermedia Design MethodsS Montero, P. Diaz, and J. Aedo 1053

Localizing XML Documents through XSLTY Yu, 1. Lu, 1. Xue, Y Zhang, and W. Sun 1059

vii

Realizing Web Designs with the Cobana FrameworkR. Gitzel, A. Karthaus, and N. Mazloumi 1065

Version Control ofXML Documents using SparseVersion StampsG. Park and C. Wu 1072

Specification of Business Process in the Context ofB2B E-CommerceY Baghdadi 1078

Software Engineering and Applications

An Investigation on Requirements ElicitationIssues in Computer-supported Collaborative Learning -Malaysian ExperienceMK. Zarinah and S.S Siti 1084

Software Quality Issues in Extreme ProgrammingS V Nagarajan, 0. Garcia, and P. Croll 1090

Software Distribution Environments for WorkflowManagement Systems - The Case of MQ SeriesWorkflowR. Anzbock. S Dustdar, and H Gall 1096

Derivation of a Measurement for Defining IdealNumber of Comments in CodeHH Yang, S Williams, and R. McCrindle 1103

Effort Estimation based on Data Structures inEntity-relationship DiagramsG.J. Kennedy 1109

A Method for Teaching a Software Process basedon the Personal Software ProcessZ. Car 1115

Generational Change Model in the Software LifeCycleE. Chang, 1. Aisbett, and N. layaratna 1121

A Generic Approach for a CORBA-LDAP GatewayImplementation for Enterprise TelecommunicationSystemsW. Radinger, M Jandl, A. Szep, andK.M Goeschka 1127

A Logical Abstract Machine for MobileCommunicating AgentsI. Moura and P. Bonzon 1133

Software Reliability and Requirements

Assessment of Software Safety Via CatastrophicEvents CoverageA.J. Kornecki 1139

Analysis and Theoretical Validation of Object-orientedCoupling Metrics1. Alghamdi and Mo. Saliu 1145

Requirement Indicators for Mobile Work: TheMOWAHS ApproachH Ramampiaro, AI Wang, c-r. Sorensen, H.N. Le,and M Nygard 1153

CM3: Problem Management: Taxonomy of ActivitiesM Kajko-Mattsson 116/

Software Modelling, Simulation, and Optimization

Convergence Assessment of the Calibration Algorithmfor the State Variable Model of the SoftwareTest Process1. W. Cangussu 1167

An Industry Proven Software Engineering ProcessModel for Small Companies1.J. Ahonen, T. lunttila, and S-K. Taskinen 1173

A Fuzzy Global Optimization Method for ParallelComputationB. Ustundag and OK Erol 1179

Syntax for TablesT. Kirishima, T. Motohashi, T. Arita, K. Tsuchida,and T. Yaku 1185

IV. DATABASES AND APPLICATIONS (DBA)

Knowledge Discovery, Data Mining, and DataWarehousing

A Test Bed for Information Retrieval ExperimentationY Zhang and V Dasigi 1191

On Modeling First Order Predicate Calculus using theElementary Mathematical Data Model in MatBaseDBMSC. Mancos. S Dragomir, and L. Crasovschi 1197

Fast Frequent Itemset Mining using CompressedData RepresentationR.P. Gopalan and YG. Sucahyo 1203

Hybrid Concurrency Control in Multilevel SecureDatabase Systems1.R. Getta 1209

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Information Retrieval, Management, andDocumentation

Establishing a Health Community KnowledgeManagement SystemC. Tatsiopoulos, G. Vardangalos, andD. Alexandrou 1215

An Arabic Auto-indexing System for InformationRetrievalR.A. Haraty, N. Mansour, and W Daher 1221

Knowledge Representation and Acquisition inSupporting Document Filing and RetrievalX. Fan, F. Sheng, G. Thomas, and P.A. Ng 1227

ADMIS: Generalized Administrative SystemS Neskovic and B. Lazarevic, and G. Soldar 1233

Web Technologies and XML

Efficient Search of Multimedia Metadata on XMLDatabase Systems using Information RetrievalApproachS Qiu and S Li 1239

Making Spatial Analysis with a DistributedGeographical Information SystemM. Torres, M. Moreno, R. Menchaca, andS Levachkine 1245

A Comparison of Structure-generic RelationalStorage Schemes ofXML DataW Zhang and D. Pollok 1251

Efficient Accessibility Lookup for XMLM. Jiang and A. w-c. Fu 1257

A Documentary View on Distributed Databases-Thanks to an XML SchemaT. Quid Braham 1263

A Comparative Performance Study of ThresholdMethods for Actual Documents Recognition Systembased on Post-PC EnvironmentS Ji, C. Yunkoo, K. Taeyuen, andK. KyeKyung 1269

ADDITIONAL PAPERS

Computer-supported Collaborative KnowledgeConstruction using KC-Space: The Approach andExperimental FindingsSS Salim and SS Abdul Rahman 1273

Evolving High-Dimensional, Adaptive Camera-basedSpeed SensorsR. Salomon 1278

PAPERS FROM OTHER lASTEDCONFERENCES:

351-366An XML Viewer for Tabular Forms for Use withMechanical Documentation0. Inoue, K. Tsuchida, S Nakagawa, T. Arita,and T. Taku 1284

362-345Preventing Computational Chaos in AsynchronousNeural NetworksJ. Barhen and V Protopopescu 1290

365-119Automated Negotiation with Multiple Concept-basedAlternatives in Multi-agent SystemsZ. Yan and S Fong 1296

374-125Domain Knowledge Representation: Using anOntology LanguageR. Lu and Z. Jin 1302

374-126Involving End Users in Requirements Elicitation andGoal-Oriented AnalysisZ. Jin and D.A. Bell 1308

AUTHOR INDEX 1314

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