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Conclusion validity: the small number of data points is not ideal from the statistical point of view, however small sample sizes are a known problem difficult to overcome. Construct validity: we measured efficiency and effectiveness using the same approach proposed in [27]. These two measures are often used in studies that investigate defect detection techniques [27], which is also our case. VII. CONCLUSION AND FURTHER WORK This paper described an observational study aimed at eliciting how the usability inspectors apply the WE-QT technique, a usability inspection technique specifically tailored for novice inspectors. Both quantitative and qualitative results of this study provided us with important feedback to improve the WE-QT technique further. The quantitative analysis showed that the effectiveness indicator computed for WE-QT v2 was higher than the effectiveness measured in the feasibility study. However, other factors may have influenced this outcome besides the improvement of WE-QT technique. Despite the positive result of the quantitative data, the qualitative analysis showed that novice inspectors still have difficulties understanding determined questions of the technique. In addition, the way subjects applied the WE-QT technique did not correspond to the natural/correct inspection flow, indicating that the inspection process is not well defined in the technique. Therefore, according to the adopted methodology, it is necessary to improve our technique and execute another observational study. Future work include: (1) improvement of the technique based on a detailed analysis of the influence of each verification in the final list of detected defects; (2) the replication of the experiment in an industrial environment, and, at last, (3) further studies comparing the WE-QT technique with other inspection techniques specific for evaluate Web applications usability. ACKNOWLEDGMENTS The authors would like to acknowledge the support granted by CAPES process AEX 4982/12-6. REFERENCES [1] G. Kappel, B. Proll, W. Retschitzegger, W. Schwinger, "Customization for ubiquitous web applications: a comparison of approaches", International Journal of Web Engineering and Technology, v. 1, pp. 79- 111, 2003. [2] E. Luna, J. Panach, J. Grigera, G. Rossi, O. Pastor, "Incorporating usability requirements in a test/model-driven web engineering approach", Journal of Web Engineering, v. 9, n. 2, pp. 132-156, 2010. [3] J. Offutt, “Quality Attributes of Web Software Applications”, IEEE Software, v. 19, n. 2, pp. 25-32, 2002. [4] M. Matera, F. Rizzo, G. T. Carughi, “Web Usability: Principles and Evaluation Methods”, E. Mendes, N. Mosley, (eds), Web Engineering, Spinger Verlag, 2006. [5] E. Mendes, N. Mosley, S. Counsell, “The need for web engineering: an introduction”, Mendes E., Mosley N. (eds.): ‘Web engineering’ Springer, pp. 1-26, 2006. [6] M. Gomes, D. Viana, L. Chaves, A. Castro, V. Vaz, et al., "WDP-RT: A usability inspection reading technique for web applications". VI Experimental Software Engineering Latin American Workshop, v. 1, pp. 124 - 133, 2009. (In Portuguese). [7] B. Bonifácio, D. Santos, C. Araújo, S. Vieira, T. Conte, " Applying Usability Inspection Techniques for Evaluating Web Mobile Applications". IX Brazilian Symposium of Human Factors on Computer Systems, v. 1., pp. 189-192, 2010. (In Portuguese). [8] A. Fernandez, E. Insfran, S. Abrahão, "Usability evaluation methods for the web: A systematic mapping study", Journal Information and Software Technology, v. 53, n. 8, pp. 789-817, 2011. [9] T. Conte, J. Massolar, E. Mendes, G. H. Travassos, “Web Usability Inspection Technique Based on Design Perspectives”, IET Software Journal, v. 3, n. 2, pp. 106-123, 2009. [10] P. Fernandes, L. Rivero, B. Bonifácio, D. Santos, T. Conte, " Evaluating a New Usability Inspection Approach: a Quantitative and Qualitative Analysis”. VIII Experimental Software Engineering Latin American Workshop, v. 1, p. 67-76, 2011. (In Portuguese) [11] F. Shull, J. Carver, G. H. Travassos, “An empirical methodology for introducing software processes”, ACM SIGSOFT Software Engineering Notes, v. 26, n. 5, pp. 288-296, 2001. [12] A. Strauss, J. Corbin, “Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory”, 2ed., SAGE Publications, 1998. [13] ISO (1997). ISO 9241-11: Ergonomic requirements for office work with visual display terminals (VDTs). Part 11 — Guidelines for specifying and measuring usability. Gènève: International Organisation for Standardisation. [14] H. Rocha, M. Baranauskas, “Design and Evaluation of H uman- Computer Interfaces”, M.C.C., 1. ed. Campinas: Emopi Publisher and Graphic, v. 1, p. 244, 2003. (in Portuguese). [15] M. Costabile, M. Matera, "Guidelines for Hypermedia Usability Inspection", IEEE Computer Society Press, v. 8, n. 1, pp. 66-69, 2001. [16] L. Triacca, A. Inversini, D. Bolchini, "Evaluating Web usability with MiLE+". VII IEEE International Symposium on, pp. 22-29, 2005. [17] M. Blackmon, P. Pols on, M. Kitajima, "Cognitive walkthrough for the web". Conference on Human Factors in Computing Systems, v. 5, pp. 463 – 470, 2002. [18] L. Filgueiras, S. Martins, C. Tambascia, R. Duarte, "Recoverability Walkthrough: An Alternative to Evaluate Digital Inclusion Interfaces". Latin American Web Congress, pp. 71-76, 2009. [19] M. Allen, L. Currie, S. Bakken, V. Patel, J. Cimino, "Heuristic evaluation of paper-based Web pages: A simplified inspection usability methodology", Journal of Biomedical Informatics, v. 39, n. 4, pp. 412 – 423, 2006. [20] A. Oztekin, A. Nikov, S. Zaim, "UWIS: An Assessment Methodology For Usability Of Web-based Information Systems", Elsevier Science Inc., v. 82, n. 12, pp. 2038-2050, 2009. [21] M. Gomes, F. Santos, D. San tos, G. Travassos, T. Conte, “Evolving a Usability Evaluation Technique through In Vitro and In Vivo Studies”. IX Brazilian Symposium on Software Quality, v. 1, pp. 229 – 244, 2010. (In Portuguese) [22] V. Basili, H. Rombach, “The tame project: towards improvementoriented software environments”, IEEE Transactions on Software Engineering, v. 14, n. 6, pp. 758 –773, 1988. [23] A. Dix, J. E. Finlay, G. D. Abowd, R. Beale, “Human-Computer Interaction”, 3rd Edition, Prentice-Hall, Inc., 2003. [24] A. Fernandez, S. Abrah, E. Insfran, "Towards to the validation of a usability evaluation method for model-driven web development", IV International Symposium on Empirical Software Engineering and Measurement, pp. 54, 2010. [25] J. Nielsen, “Heuristic evaluation”, In: Jakob Nielsen, Mack, R. L. (eds), Usability inspection methods, Heurisitic Evaluation, John Wiley & Sons, Inc, 1994. [26] C. Wöhlin, P. Runeson, M. Höst, M.C. Ohlsson, B. Regnell, A. Wessl, “Experimentation in software engineering: an introduction”, Kluwer Academic Publishers, 2000. [27] C. Denger, R. Kolb, “Testing and Inspecting Reusable Product Line Components: First Empirical Results”, V International Software Metrics Symposium, 2006. 593
Identification Guidelines for the Design of Interfaces in the context of ECAs and ADHD Sandra Rodrigues Sarro Boarati FACEN Methodist University of Piracicaba Piracicaba, SP srsarro@unimep.br Cecília Sosa Arias Peixoto FACEN Methodist University of Piracicaba Piracicaba, SP cspeixot @unimep.br Abstract— The HCI (Human-Computer Interaction) aims to seek a friendly interaction between man and computer. To demonstrate it, it was studied in this paper the Cognitive Styles of Learning and users suffering from attention deficit disorder aiming at improvements in software development. The user population is diverse, there is a mixture of multiple profiles of users who need to somehow get their needs met and so this is why the system interface should be created in accordance with the diversity of users. Realizing this gap, this study examined the learning styles and attention deficits, allowing to generate a series of recommendations, guidelines, which are best suited to specific characteristics of the profiles of users. These recommendations could be applied in the construction of new interfaces that will be adjusted to different user profiles, and the use of these recommendations will contribute to greater user satisfaction respecting the different characteristics between them. Keywords - Human-Computer interaction; Cognitive Users Profiles; Guidelines for design interfaces. I. INTRODUCTION The HCI (Human-Computer Interaction) is a “discipline that concerns with the design, evaluation and implementation of interactive computing systems for human use and the study of major phenomena surrounding them. The goals of HCI are to produce usable systems, safe and functional "[1]. The interaction between man and machine takes place through the computer interface [2]. In the quest to bring the user interface, cognitive engineering, which is the process by which one acquires knowledge, is used in order to improve software development. During the design phase of the interface are necessary to incorporate recommendations in order to build highly usable interfaces. Plasaint and Shneiderman [3] reported that a successful interface design should be based on recommendations from the project, called guidelines. For Nielsen [4], guidelines are list of principles to be followed in the development project. Nielsen proposes the use of a phase specifies guidelines (Guidelines and heuristic evaluation) life cycle, focusing on interface design, called Usability Engineering Lifecycle [4], given the importance of the recommendations during the design of an interface. Authors such as Nielsen [6], Shneiderman and Plaisant [3] reported several design guidelines in their work and make several recommendations on how the interfaces should be for children, elderly, etc. However most of the recommendations touch only isolated aspects of the characteristics of users. Then there is a large gap in this area. There is more than one skill or characteristic to be understood and it stimulate the development of this study of the Cognitive Styles of Learning (CSL) from the perspective of Souto [7] and attention deficit, allowing to study a series of recommendations, guidelines for the design of interfaces and to consider more deeply the users profile or their skills. To build the user profile is necessary first the diagnosis of the model user needs, as well as their cognitive, cultural and physical situations. This information is useful to provide the best interaction between the various information systems [8]. This research focused on adding guidelines and rules of selecting to the expert system GuideExpert from Cinto and Peixoto [5], just an area where researches are fairly recent and not yet cataloged in a joint manner. The GuideExpert system has the function to suggest and propose guidelines for interface design. This paper is organized into five sections: Section 1 presents the introduction. Section 2 presents the user and their role in the requirements specification; the interface; the diversity of users and the identification of learning styles. Section 3 presents the system GuideExpert, which is a tool that has the function to suggest and propose guidelines for interface design in its previous version and new recommendations suggested to improve the tool. Section 4 presents the analysis and implementation of some recommendations, guidelines of this work, sites for children and elderly users. Section 5 presents the conclusions. II. THE USERS AND THEIR DIVERSITY OF PROFILES The user population is not a system composed of only one type of user. In general, there is a mixture of multiple profiles of users who need to somehow get their needs met [2]. Speaking of users interacting with computers, we refer to the user’s knowledge that should be taken into account in the design of a n HCI. Below are some features that must be observed during the interface design [6], [3]: • The presence of an internationalized system or used in more than one country or region. Each country or region has its own peculiarities. Dialects, cultures, ethnicities, races, etc. All these elements end up generating needs that to be satisfied; 594
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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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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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To validate the proposed approach,
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contains broadcast of Subject to Ob
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S 1a=1; S 2b=5; cobegin { S 3read
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.
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298
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A functionality is the ability of a
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level); in UML/J2EE technology [11]
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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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Page 615 and 616: that automatically presents the usa
Page 617 and 618: Improving a Web Usability Inspectio
Page 619 and 620: Following the experimental methodol
Page 621: About the category “Improvements
Page 625 and 626: created according to the preview re
Page 627 and 628: In our analysis, the “Government
Page 629 and 630: In the following, we present the re
Page 631 and 632: USE SCENARIO The application Vuelos
Page 633 and 634: [8] Mayhew, D., “The Usability En
Page 635 and 636: Interaction State Set Structure Dat
Page 637 and 638: B. Component Model Fig. 2 show s th
Page 639 and 640: As a continuation of our research w
Page 641 and 642: PROMETHEE methods [3] belong to a f
Page 643 and 644: comparison rule, a value of 1/9 is
Page 645 and 646: TABLE V: Supermatrix for the exampl
Page 647 and 648: for knowledge sharing. Based on str
Page 649 and 650: y applying hash functions to its su
Page 651 and 652: Empirical Validation of Variability
Page 653 and 654: III. EXPERIMENTAL STUDY In this sec
Page 655 and 656: Table III SPEARMAN’S CORRELATION
Page 657 and 658: A Mapping Study on Software Product
Page 659 and 660: most relevant sources in software e
Page 661 and 662: TABLE IV TOOL’S CLASSIFICATION AC
Page 663 and 664: [2] P. Clements and L. Northrop, So
Page 665 and 666: and future works. II. BACKGROUND ON
Page 667 and 668: System Advanced Standard Module A M
Page 669 and 670: Algorithm 1: Backtracking for MIN-A
Page 671 and 672: outcomes from such research fields,
Page 673 and 674:
TABLE IV. CONTINUED FROM PREVIOUS P
Page 675 and 676:
contribution of each study was made
Page 677 and 678:
assets, the plugin approach can be
Page 679 and 680:
Figure 3: PlugSPL Feature Model Edi
Page 681 and 682:
contributions from the several acto
Page 683 and 684:
o Guideline 6.4: Softgoal dependenc
Page 685 and 686:
descriptions must also b e configur
Page 687 and 688:
It means that throughout the develo
Page 689 and 690:
B. Instrumentation The experiment w
Page 691 and 692:
• External Validity: W e have con
Page 693 and 694:
II. THE PROPOSED TAXONOMY The taxon
Page 695 and 696:
sub-dimension is categorized as, co
Page 697 and 698:
A Proposal of Reference Architectur
Page 699 and 700:
Figure. 1. Reference Architecture M
Page 701 and 702:
A Variability Management Method for
Page 703 and 704:
C. Correctness of configuration fil
Page 705 and 706:
means patterns which are shown in s
Page 707 and 708:
Tool Support for Anomaly Detection
Page 709 and 710:
Figure 1. System overview of the SD
Page 711 and 712:
Once the datasets were evaluated us
Page 713 and 714:
Reconfiguration of Robot Applicatio
Page 715 and 716:
data dependencies required for keep
Page 717 and 718:
Spacemaker: Practical Formal Synthe
Page 719 and 720:
Fig. 1. The ecommerce object model.
Page 721 and 722:
Fig. 3. Mapping diagram for Figure
Page 723 and 724:
A formal support for incremental be
Page 725 and 726:
machine may be empty which should l
Page 727 and 728:
software, based on revised requirem
Page 729 and 730:
A Process-Based Approach to Improvi
Page 731 and 732:
Appropriate processes m ust support
Page 733 and 734:
Figure 2. Reordered layers of the k
Page 735 and 736:
Automatic Acquisition of isA Relati
Page 737 and 738:
III. VALIDATING THE DIMENSION HEADE
Page 739 and 740:
The table title and the dimension s
Page 741 and 742:
A light weight alternative for OLAP
Page 743 and 744:
i.d. subsets of cubes focused on se
Page 745 and 746:
Match production rules Enterprise S
Page 747 and 748:
A Tool for Visualization of a Knowl
Page 749 and 750:
other ontology visualization tools
Page 751 and 752:
shown at Figure 5. Objectives are s
Page 753 and 754:
Rendering UML Activity Diagrams as
Page 755 and 756:
a = O1 → a → O2 b = O2 → b
Page 757 and 758:
ecordProblem → A → reproducePro
Page 759 and 760:
umlTUowl - A Both Generic and Vendo
Page 761 and 762:
The tool’s ability to deal with S
Page 763 and 764:
Elem. UML Example D 12 Harmonizing
Page 765 and 766:
4) Associations: In the first test
Page 767 and 768:
III. GRAPH REPRESENTATION OF CLASS
Page 769 and 770:
as an add-in to popular UML m odeli
Page 771 and 772:
742
Page 773 and 774:
744
Page 775 and 776:
746
Page 777 and 778:
her necessities. However, the progr
Page 779 and 780:
Figure 3. Global Log. of terms. The
Page 781 and 782:
two stages. The first stage is focu
Page 783 and 784:
754
Page 785 and 786:
756
Page 787 and 788:
758
Page 789 and 790:
With the GDUC approach, we can mode
Page 791 and 792:
Figure 6. Three proposals containin
Page 793 and 794:
764
Page 795 and 796:
766
Page 797 and 798:
Proactive Two Way Mobile Advertisem
Page 799 and 800:
information. If more than one adver
Page 801 and 802:
Users can al so publish adv ertisem
Page 803 and 804:
The COIN Platform: Supporting the M
Page 805 and 806:
A-3
Page 807 and 808:
Checking Contracts for AOP using XP
Page 809 and 810:
Maicon B. da Silveira, 112 Aldo Dag
Page 811 and 812:
Seyedehmehrnaz Mireslami, 70 Takao
Page 813 and 814:
Wei Zhang, 422 Wenbo Zhang, 188 Zhi
Page 815 and 816:
Desmond Greer Eric Gregoire Christi
Page 817 and 818:
Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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