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through bias shifting perform better than algorithms with static bias under certain conditions [12]. The performance criteria for biases include: predictive accuracy of learned hypothesis, efficiency in a bias guiding through the learning process, and readability of biases [5]. Finally, it should be noted that there is a d iscrepancy between the aforementioned machine learning bias and the statistical bias [13]. O ur focus in this paper i s on the machine learning bias. The bias shifting process in our work is triggered by the occurrence of inconsistency and carried out as an online evaluation of potential candidate biases. Given a bias as consisting of a f eature set, the shifting methods are bas ed on strengthening or weakening a bi as through adding or rem oving features with regard to the initial feature set. T he performance criterion for the bias shifting is to improve the predictive accuracy of the existing hypothesis so as to overcome inconsistencies. Definition 4. A bias b is consistent with regard to if the hypothesis hb obtained via b has the following property: hb (one of the elements in , not both) The bias shifting process itself must have a bias which is in meta-bias space [5]. In the proposed framework, the meta-bias that underpins the i 2 Learning process is referred to as Maximum Consistency-Minimum Shifting, or MC MS for short. The existing bias b in the previous learning episode contains a set of features that produced a model that does not support generalization in a co nsistent manner, as evidenced by the set o f inconsistent literals derived during problem solving. Perpetual learning is embodied in the continued process of overcoming inconsistencies, which in turn relies on bias shifting so as to refine the previously learned model. Bias shifting amounts to refining the feature set used in defining the bias. The current bias b can be either strengthened or w eakened through adding or removing feature(s) from its defining set. The minimum shifting is embodied in removing or adding one feature at a time to revise b. The objective is to have a refined bias b that is consistent. 4.3. i 2 Learning(DEC, mex) We use the iterative deepening search [11] to carry out the bias shifting process. The feature set for the current bias b is the starting node in the s earch space. Removing a feature from, or adding a feature to, b generates a successor node (child node) b of b. If b results in a hypothesis hb that produces a consistent generalization to circumvent the inconsistency manifested in , then b is retu rned as th e refined bias. hb will guide the refinement process to KB and mKB. On the other hand, if all the possible successor nodes obtained through removing/adding one feature from b cannot lead to a hypothesis that produces a cons istent generalization to resolve the inconsistency in , then the iterative deepening search will repeat t his process by generating the successor nodes to b. The depth of the search for a con sistent bias is iteratively deepened, hence capturing the essence of minimum shifting. Each time when a new is encountered during the agent’s problem solving episode, the learning module is engaged in overcoming the inconsistency continuously, which enables the continuous knowledge refinement that incrementally improves the agent’s performance at its tasks, the essence of perpetual learning. We define the following algorithms to be used by i 2 Learning(DEC, mex): o IDBR(bias): for iterative deepening bias refinement, which takes a bias (feature set) as its input and returns either a co nsistent bias or failure. Here we only consider the feature-removal case in bias refinement. o RFR(node, limit): for recursive feature removal, which takes a feature set and a search depth as its input and returns either a consistent bias or failure/leaf. o biasTest(node): for checking if a given feature set can be a consistent bias, which takes a feature set as its input and returns the truth value of true or false. o sucSet(node): for creating a successor set for a given feature set, which takes a feature set as its input and returns a successor set. Algorithms for i 2 Learning(DEC, mex) Input: , Ã, KB, mKB, , , bias (feature set); Output: bias, a consistent bias; hb, refined model; 1 IDBR(bias) { 2 for depth = 0 to |bias|–1 do { 3 result = RFR(bias, depth); 4 if (result leaf) then {return result} 5 } 1 RFR(node, limit) { 2 if (biasTest(node)) then 3 {return node} //consistent bias 4 else if (limit=0) then {return leaf} 5 else { 6 leaf_Reached=false; 7 for each successor in sucSet(node) do { 8 result=RFR(successor,limit-1); 9 if (result=leaf) then {leaf_Reached=true} 10 else if (resultfailure) then 11 {return result} 12 } 13 if (leaf_Reached) then {return leaf} 14 else {return failure} 15 } 16 } 1 biasTest(node) { 2 obtain h node ; 3 if (h node yields consistent generalizations) 4 then {return true} 5 else {return false} 6 } 1 sucSet(node) { 2 successorSet = ; 3 for each feature in node do { 4 successorSet=successorSet {node–feature}; 5 } 6 return successorSet; 7 } 253
4.4. An Illustrative Example Now let us look at an illustrative example. Assuming we have the following KB for classifying animals and plants that is obtained based on a bias b of three features in Ã = {Shape, Cell_Walls, Cell_Functions} where Shape is ab out an object’s morphology, Cell_Walls pertains to cell walls being non-rigid or rigid, and Cell_Functions defines if cells break sugar down to carbon dioxide or turn carbon dioxide into sugar. We rewrite b = { S, CW, CF} where S stands for Shape, CW for Cell_Walls, and CF for Cell_Functions. R1: CucumberShape(x) SeaCucumber(x) R2: Cucumber(x) CucumberShape(x) R3: Plant(x) Cucumber(x) R4: NonRigidCellWalls(x) SeaCucumber(x) R5: SugarToCarbonDioxide(x) SeaCucumber(x) R6: Animal(x) NonRigidCellWalls(x) R7: Animal(x) SugarToCarbonDioxide(x) R8: Animal(x) RigidCellWalls(x) R9: Animal(x) CarbonDioxideToSugar(x) R10: Plant(x) RigidCellWalls(x) R11: Plant(x) CarbonDioxideToSugar(x) R12: Plant(x) NonRigidCellWalls(x) R13: Plant(x) SugarToCarbonDioxide(x) R14: RigidCellWalls(x) NonRigidCellWalls(x) R15: CarbonDioxideToSugar(x) SugarToCarbonDioxide(x) R16: NonRigidCellWalls(x) RigidCellWalls(x) R17: SugarToCarbonDioxide(x) CarbonDioxideToSugar(x) Given a seacucumber sc as an input ={SeaCucumber(sc)}, we obtain Plant(sc) from {R1, R2, R3}, and Animal(sc) from {R4, R5, R6, R7}. The given instance of sc cannot be a plant and an animal at the same time, hence = {Animal(sc), Plant(sc)} and Animal(sc) Plant(sc). We know that there is one piece of supportive evidence for Plant(sc) (via R3 R2 R1) and two pieces of supportive evidence for Animal(sc) (via R6 R4 and R7 R5). In addition, we can also obtain two pieces of negative evidence about Plant(sc) (or two pieces of positive evidence about Plant(sc)) (via R12 R4 and R13 R5). The issue becomes how to refine b through minimum shifting to generate a co nsistent bias b. By calling IDBR with b={S, CW, CF}, the iterative deepening bias refining process takes place with regard to the search space in Figure 4. The minimum shifting process will result in b = {CW, CF} being returned as a consistent bias. Table 1 shows the supports for elements in the successor set of b. In addition, the mKB will be augmented with the following control information such that next time when an input i includes a ground atom for SeaCucumber(x), derivations of {R 6 R 4 , R 7 R 5 } are preferred and the derivation of {R 3 R 2 R 1 } will be circumvented. (): {6 4, 7 5} {3 2 1} Figure 4. Search space. Table 2. Support of {CW, CF} for Plant(sc) and Animal(sc). CellWalls CellFunction Plant(SC) 0 0 0 Plant(SC) 0.5 0.5 1.0 Animal(SC) +0.5 +0.5 +1.0 Animal(SC) 0 0 0 5. Conclusion Total Support In this paper, we describe i 2 Learning, a framework for perpetual learning agents. i 2 Learning allows the learning episodes of the agent to be initiated by inconsistencies the agent encounters during its problem-solving episodes. Learning in the framework amounts to th e continuous knowledge refinement and/or augmentation in order t o overcome encountered inconsistencies. An agent’s performance at tasks can be incrementally improved with each learning episode. i 2 Learning offers an overarching structure that accommodates the growth and expansion of various inconsistency-specific learning strategies. Through the mutually exclusive inconsistency, we demonstrate algorithmically how i 2 Learning facilitates learning in terms of bias shifting. The main contributions of this research work include the following. We dem onstrate that learning through overcoming inconsistency is a viable and useful paradigm. The i 2 Learning framework accommodates various inconsistency-specific heuristics to be deployed in the continuous learning process. Through iterative deepening bias shifting, an agent’s performance can be incrementally improved by overcoming instances of mutually exclusive inconsistency. Future work can be carried ou t in the following directions. Experimental work is needed on the iterative deepening bias shifting process for the mutually exclusive inconsistency case. Details of other frequently encountered inconsistencies and their respective learning heuristics still need to be fleshed out. Acknowledgements. We express our sincere appreciation to the anonymous reviewers for their comments that help improve the content and the presentation of this paper. 254
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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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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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• to provide a unified catalogue
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flexibility they provide to model s
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complete in zero-time, and thus hav
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figure assumes the experiment with
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could be a practical way of dealing
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and (iv) the reduced amount of line
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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(r 1 r 2 ...r k ) 1 denotes Path [r
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Fig. 3. Example a 3-compound weakly
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necessary before verification. In t
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CTL formula as: AG (( jp EX( jp ad
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equirements level, like EA-Miner [1
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II. RESOURCE-ORIENTED WORKFLOW MODE
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eventually triggers a co mmon task.
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Input: A resource oriented workflow
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access control exists, the action i
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(ACCDA), is customized to address a
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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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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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