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support because S S S which means that S is the union 3 1 2 3 of other supports. III. PROPERTIES OF MINIMAL SUPPORTS OF S-INVARIANTS In this section, we will give a suff icient and necessary condition for a place s ubset to be a m inimal support of S- invariants. Before that, some properties are given below. Lemma 1 [12] If S is an support of S-invariants, but not a 0 minimal support, then there are at least t wo minimal supports of S-invariants S , S ,..., S 1 2 k 2 k such that S S S ... S . 0 1 2 k Lemma 2 Let C be an m* n ordered integer matrix and the rank of C satisfies RC n . Then the homogeneous equation CX 0 has a fundamental system of solution in which all the base solutions are integer vectors. Proof: Because RC n and the coefficient matrix C is an integer matrix, using Gaussian elimination method, we can get a fu ndamental system of solu tion which is co nsisted of rational vectors for the homogeneous equation CX 0 . We denote the system by = , ,..., 1 2 k . Because 1 is a ration al vector, so we can suppose that i i k i1 i2 in ... i i1 i2 in all integers and v ,..., v are nonzero. Let v i n ij j 1 i1 and T in , where ,..., and v ,..., v are i1 n n n ' v v v i i i i1 ij i2 ij in . ij j1 j1 j1 j1 j2 jn ' Then it is eas y to see th at i is an in teger vector. And as a constant times of the base solution vector for the i ' homogeneous equation CX 0 , is surely to be a base solution of CX 0 . As a result, ' ' , ' ,..., ' 1 2 k i in constitute .■ a fundamental system of integer solution of CX 0 Definition 1 [4] Let A be an incidence matrix of a Petri net ( ST , ; FW , , M) A 1 i S denotes the i th , and 0 column vector in A. For a given place s ubset S s , s ,..., s 1 j1 j2 j k S , A A , A ,..., A S1 j1 j2 j k , a sub-matrix of A, is called the generated sub-matrix corresponding to S . 1 Theorem 1 Let S 1 s , s ,..., s j1 j j k 2 i i1 be a place su bset, in T A S 1 be the generated sub-matrix corresponding to S .If the rank of 1 A S 1 satisfies R( A ) S 1 S1 1 and A Y 0 has positive S1 S1 integer solutions, then S is a minimal support of S-invariants. 1 Proof: Since A Y S1 S1 has positive integer solutions, S1 0 must be a su pport of S -invariants. Assuming that S is not a 1 minimal support of S-invariants, then there are at leas t two minimal supports of S-invariants S , S ,..., S ( k 2) such 11 12 1k that S S S ... S according to Lemma 1. Let Y 1 11 12 1k i and Y ( j 1 i, jk i j ) be the minimal S-invariants supported by S and S respectively. It’s obviously that 1i 1 j Y i and Y are linearly independent and AY AY 0 j i j . Denote the l th element of Y by Y i i l . If Yi l 's corresponding place s does not belong to S , then Y l 1 l 0 i because the support of S-invariant Y , i.e. S , is a su bset of i 1i S . Deleting all those elements whose corresponding places 1 don’t belong to S from Y , and denoting the resulted vector 1 i by , then A 0 holds because i S1 i is constructed by i deleting some zero ele ments from Y and AY 0 i i holds.Similarly, deleting all tho se elements whose corresponding places don’t belong to S from Y , and 1 j denoting the resulted vector by , then the equation j A 0 holds. S1 j In addition, because Y and Y are linearly independent, i j i and are obtained by deleting the same zero elements from j Y and Y respectively, so i j and must be linearly i j independent too. F urthermore, from A 0 S1 i and A 0 S1 j we can see and are two linear independent solutions of i j A Y 0 S1 S1 . Only when R( A ) S 2 S1 1 , A Y 0 S1 S1 can have such two independent solutions. But this is contradictory with the assumption R( A ) S 1 S1 1 in the theorem. So S must be 1 a minimal support of S-invariants. ■ s 1 t 5 t 6 t 7 t 4 t 2 s 2 s 5 s 3 s 4 s 7 t 8 t 3 s 6 t 1 Fig 2. A Petri net example 2 Now let’s verify Theorem 1 w ith Peri n et in Fig.2. In 2 fact, has two minimal supports of S-invariants, which are 2 S s , s , s , s , s S s , s , s , s , s respectively. and 1 1 2 5 6 7 2 3 4 The generated sub-matrix S s , s , s , s , s is as follows. 1 1 2 5 6 7 5 6 7 A S 1 corresponding to 341
A S 1 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 r2 r1 0 0 0 0 0 0 0 0 0 0 r8 r7 0 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 After two elementary-row-transformations listed above, it’s easy to see th at the rank of satisfies R( A ) 4 S 1 AS 1 S1 1 and Y 2 2 1 1 1 T is a positiv e integer solution of S1 A Y 0 S1 S1 . According to Theorem 1, S is a minimal support 1 of S-invariants. This is consistent with the facts. A S 3 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 1 1 0 r2 r1 0 0 1 1 0 r2 r5 r4 r3 0 0 1 1 0 0 0 0 0 1 r4 r6 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 However, for S s , s , s , s , s , its corresponding 3 1 2 3 4 5 generated sub-matrix A S 3 is given above. After four elementary-raw-transformations, it’s easy to see that the last element in any solution of A Y 0 S3 S3 must be 0 acco rding to the 4 th line in the ladder-type matrix. So A Y 0 has no S3 S3 positive integer solutions. According to Theorem 1, S isn’t a 3 minimal support of S-invariants. This is correct again. Theorem 2 If S is a minimal support of S-invariants, then the 1 rank of AS 1 satisfies R( A ) S 1 and A Y 0 S1 1 S1 S1 has positive integer solutions. Proof: Let Y be the minimal S-invariant supported by S 0 1 , be the sub-vector of Y corresponding to S . Obviously, 0 1 is a positiv e solution of A Y 0 according to the S1 S1 definition of minimal S-invariants. Next, we only need to prove R( A ) S 1 S1 1 . Assuming that R( A ) S S1 1 , then A S 1 is a full-column-rank matrix. According to [10], the equation A Y 0 has only S1 S1 zero solutions when A S 1 is full-column-rank. It is contradictory with the above conclusion that is a positive solution of A Y 0 S1 S1 . Assuming that R AS 1 S 1 1 , according to Lemma 2, the equation A Y 0 has a fundamental system of integer S1 S1 solution, denoted by = , ,..., 1 2 k ,where k 2 . 1) On one hand, we will prove that any base solution in isn't constant times of . Otherwise, assuming that i satisfies c ( c R c 0 ). Since the linear i independence among base s olutions, (1 j k j i) j isn’t constant times of . So for any positive integers and 1 , ' isn’t constant times of . 1 i 2 j 2 However, because and are both positive integer i j solutions of A Y 0 S1 S1 , so ' is also a 1 i 2 j positive integer solution vector of A Y 0 . So ' S1 S1 corresponds to an S-invariant supported by S . By now, an S- 1 invariant corresponding to ' is obtained, which isn’t constant times of the minimal S-invariant corresponding to . Both of them are supported by the same minimal support S . However, 1 it has been proved in [4] that a ny S-invariant is a constant multiple of the minimal S-invariant when they are supported by one minimal support of S-invariants. This contradiction indicates that any fundamental system of integer solution of A Y 0 should contain no base solutions which are S1 S1 constant times of ; 2) On the other hand, as a solution of A Y 0 S1 S1 , should have the form that ... ------(1) 1 1 2 2 k k where , ,..., are constants. 1 2 k As has been proved that each vector in = , ,..., 1 2 k can’t be constant times of , so there are at least two nonzero constants in , ,..., 1 2 k . Assuming that 0 , then the i following equation holds: k ( ) / ------(2) i j j i j 1 j i According to (1) an d (2), ' , ,... , , ,..., 1 2 i-1 i1 t and = , ,..., 1 2 t are equivalent. So ' is also a fundamental system of integer solution of A Y 0 S1 S1 . However, ' contains vector , which is contradictory to t he conclusion obtained in the last sentence (shown in bold) of R A S is not true. part 1) in this proof. So S 1 1 1 To sum up, neither R AS 1 S1 nor which indicates R( A ) S 1 S1 1 .■ R A S holds S 1 1 1 In Fig.2, S 1 s , s , s , s , s 1 2 5 6 7 is a minimal support of S- invariants, so R( A ) S 1 holds and A Y 0 has a S1 1 S1 S1 positive integer solution Y 2 S1 2 1 1 1 T , which is consistent with Theorem 2. Now we can get a sufficient and necessary condition for a place subset to be a minimal support of S-invariants as follows. 342
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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“iphone” and “blackberry” a
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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elicitation and the act ivities con
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Step 2. Step 3. 1.2. Use of Procedu
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Figure 6.b. Task: Cognitive Analysi
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original User’s Discourse / Speec
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II. RELATED WORK Business processes
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test, we did not find any significa
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For the indicator “requirements a
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the model checkers SPIN [8] and NuS
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allow for the presentation of syste
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the requirement or design phases sh
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using the CR estimates against the
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Data Set TABLE II. DATA SETS Artifa
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Figure 4. Relative Error in the Est
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Take basic requirements from user a
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sent Semantic Web Ontologies. It co
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The rest of the paper is organized
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and the risk level. Existing assess
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diagram could give developers an in
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Figure 2. Co
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• Sensor(Lexical): at the top and
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An Overview of the RSLingo Approach
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Figure 2. Overview of the RSLingo a
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DETECTING EMERGENT BEHAVIOR IN DIST
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Definition 2: For a set of MSCs M,
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Stability of Filter-Based Feature S
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Algorithm 1: Threshold-Based Featur
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MI KS GM AUC PRC S2N RUS35 RUS50 RO
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80
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82
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84
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86
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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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Fig. 2. UML Activity Diagram modeli
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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146
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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(a) printtok (b) printtok2 (c) sche
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Gupta extended HGS and proposed the
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manage intellectual knowledge with
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D. Manage Training When any trainin
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TABLE II. COMPARISON BETWEEN THE EX
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B. Trace Semantics of DAP Fig. 1. A
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∀ [DR ′ ] []gen [; ] [D]□ [;
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transformation idea in MDA [13] . T
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MappingRule MappingTGtoHP { [Rule I
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executing a continuous transition,
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protocol of DATS we used is properl
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in Boolean search and reasoning has
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to be broken by expelling one of it
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where, among other things: 1. The t
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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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integrate the test results. • Aut
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No tify the cloud controller to get
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of execution-data classification. T
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Figure 1. Boxplots of average AUC r
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anches is more applicable than the
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Page 411 and 412: II. RESOURCE-ORIENTED WORKFLOW MODE
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ICrudSchema and shown in Figure 3.
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Connectors for Secure Software Arch
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Non-repudiation security service pr
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anAsynchronousCustomer InterfaceCon
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How Social Network APIs Have Ended
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OSN Social Feed Unique Features Con
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users can control the reach of thei
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Computer Forensics: Toward the Cons
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In 2009, Cohen et al. in [4] have o
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dcterms:contributor, dcterms:creato
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A Holistic Approach to Software Tra
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Figure 1. Figure 1 shows an overvie
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B. Case study in a proprietary proj
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Pointcut Design with AODL Saqib iqb
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system attributes, methods and exec
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Feature modeling and Verification b
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hasOptionalPart hasPart hasMandato
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A Context Ontology Model for Pervas
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Figure 1. UML view of the model. Th
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Figure 3. Architecture for deliveri
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Ontology-based Representation of Si
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Figure 2. Upper Ontology fragment f
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I2Sim simulation model was transfor
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An ontology-based approach for stor
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fying the equivalences between conc
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• Homonymy conflicts: occur when
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Automatic Generation of Architectur
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we dealt w ith the verticals rules.
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Towards Architectural Evolution thr
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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480
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482
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484
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A Tiny Specification Metalanguage W
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C. Syntax Extensions The expressive
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however, does not allow variables f
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derivation dependencies. The third
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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Figure 4. The abstractions extracte
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way to detect something which could
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Algorithm 1: Algorithm to recursive
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obtain the input of experts on desi
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A. Pattern Reuse In patterns reuse
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IV. SCATTER In order to enhance pat
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These latter would be clustered acc
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described or even r etrieved, thus
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DC2AP Element and their Application
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21. Consequences Example of use DC2
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Legacy2Cloud logic. In this scope,
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2.3.1 Complementarity of MDD techno
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3.2.2 EAggregateRelationship The EA
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cess. The proposal follows the ADM
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II. OVERVIEW OF MODAL TRANSITION SY
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(1) V □ (A) ⊆ V □ (B), V ♦
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Suppose P = 〈S P , SP i , AI P ,
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Adaptive software should basically
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A. Step S1 - Define the Business Pr
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F. Step S6 - Monitor at Run-Time Th
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Figure 1, a metamodel defines an XM
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Figure 3 is a feature diagram that
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ecause the system of C-net model is
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Pub-T Tc Tc Sub-T Sub-T Figu
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Visual Studio Achievements, a Visua
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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TABLE IV TOOL’S CLASSIFICATION AC
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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It means that throughout the develo
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B. Instrumentation The experiment w
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• External Validity: W e have con
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II. THE PROPOSED TAXONOMY The taxon
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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Figure. 1. Reference Architecture M
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A Variability Management Method for
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C. Correctness of configuration fil
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means patterns which are shown in s
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Tool Support for Anomaly Detection
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Figure 1. System overview of the SD
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
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data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 1. The ecommerce object model.
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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software, based on revised requirem
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A Process-Based Approach to Improvi
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Appropriate processes m ust support
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Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
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III. VALIDATING THE DIMENSION HEADE
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The table title and the dimension s
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A light weight alternative for OLAP
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i.d. subsets of cubes focused on se
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Match production rules Enterprise S
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A Tool for Visualization of a Knowl
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other ontology visualization tools
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shown at Figure 5. Objectives are s
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Rendering UML Activity Diagrams as
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a = O1 → a → O2 b = O2 → b
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ecordProblem → A → reproducePro
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umlTUowl - A Both Generic and Vendo
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The tool’s ability to deal with S
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Elem. UML Example D 12 Harmonizing
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4) Associations: In the first test
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III. GRAPH REPRESENTATION OF CLASS
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as an add-in to popular UML m odeli
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742
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744
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746
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her necessities. However, the progr
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Figure 3. Global Log. of terms. The
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two stages. The first stage is focu
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754
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756
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758
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With the GDUC approach, we can mode
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Figure 6. Three proposals containin
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764
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766
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Proactive Two Way Mobile Advertisem
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information. If more than one adver
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Users can al so publish adv ertisem
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The COIN Platform: Supporting the M
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A-3
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Checking Contracts for AOP using XP
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Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
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Wei Zhang, 422 Wenbo Zhang, 188 Zhi
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Desmond Greer Eric Gregoire Christi
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Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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