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p 1 5 t 5 t 1 p 6 2 p 9 p 2 p 5 t 2 4 t 6 r p 2 3 1 p 7 t 3 p 10 t 7 r 3 p 8 p 11 2 t 4 p 4 r 1 t 10 4 p 5 p 4 p 16 t 4 p 8 t 5 t 6 p 6 4 p 1 p 3 p 2 t 1 t 2 t 3 p 15 p 14 p 13 p 7 p t 9 p 7 10 p 11 t 8 p 12 t 9 t 8 Fig. 2 Example weakly 2-compound siphon. 0 = 1 + 2 - 3. Fig. 1. Example S 3 PR with stronglydependent siphon. η 3 = η 1 + η 2 . Fig. 2. Example weakly 2-compoundsiphon. η 0 = η 1 + η 2 − η 3 . TABLE I TYPES OF SIPHONS FOR THE NET IN FIG. 1 SMS [S] η Set of places c S 1 {p 2 ,p 7 } [−t 1 + t 2 + t 6 − t 7 ] {p 9 ,p 10 ,p 3 ,p 6 } c 1 =[p 9 t 6 p 10 t 2 p 9 ] S 2 {p 3 , p 8 } [-t 2 + t 3 + t 7 − t 8 ] {p 10 ,p 11 ,p 4 ,p 7 } c 2 =[p 10 t 7 p 11 t 3 p 10 ] S 3 {p 2 ,p 3 ,p 7 ,p 8 } [−t 1 + t 3 + t 6 − t 8 ] {p 9 ,p 10 ,p 6 ,p 11 ,p 4 } c 3 = c 1 oc 2 TABLE II FOUR SMS IN FIG. 2 AND THEIR η. η 4 = η 1 + η 2 − η 3 . SMS [S] η Set of places c S 1 {p 2 ,p 3 ,p 8 ,p 9 ,p 10 ,p 11 } [+t 2 − t 4 + t 8 − t 9 ] {p 4 ,p 12 ,p 13 ,p 14 ,p 15 } c 1 =[p 15 t 2 p 14 t 3 p 13 t 8 p 15 ] S 2 {p 3 ,p 4 ,p 7 ,p 8 ,p 9 ,p 10 } [+t 1 − t 3 + t 7 − t 10 ] {p 5 ,p 11 ,p 14 ,p 15 ,p 16 } c 2 =[p 14 t 4 p 16 t 1 p 15 t 2 p 14 ] S 3 {p 3 ,p 8 ,p 9 ,p 10 } [+t 2 − t 3 − t 4 + t 7 ] {p 4 ,p 11 ,p 14 ,p 15 } c 3 =[p 15 t 2 p 14 t 6 p 15 ] S 4 {p 2 , p 3 , p 4 , p 7 , p 8 , p 9 , p 10 , p 11 } [+t 1 + t 8 − t 9 − t 10 ] {p 5 ,p 12 ,p 13 ,p 14 ,p 15 ,p 16 } c 4 = c 1 ⊕ c 2 Thus, we prove the uniform formula for weakly n- compound siphons. It also holds for strongly dependent siphons as shown earlier. Thus, the uniform formula holds irrespective to whether the compound siphon is strongly or weakly. This further enhances the uniformity of the formula. A more complicated example in the next section illustrates this for a weakly 3-compound siphon. V. EXAMPLE This section employs Fig.3 to demonstrate Theorem 4. As shown in Tables III-VII, S, [S] and η for weakly compound siphons all share the same formula verifying Theorem 4. For strongly dependent siphons, the same formula also holds except S ij =[S ij ]=∅ , η ij =0,∀i ≠ j. VI. CONCLUSION In summary, we propose a uniform formula to compute SMS, their complementary set and characteristic T-vectors for both strongly and weakly n-compound siphons based on the same underlying physics. We further propose to generalize it to a compound siphon consisting of n basic siphons. This helps to retain the formula in brain without consulting the references and simplify the computation plus its implementation to reduce the lines of codes. Future work can be directed to large S 3 PR and more complicated systems. REFERENCES [1] Wang, Y., Kelly, T., Kudlur, M., Lafortune, S., and Mahlke, S.A., “Gadara: Dynamic deadlock avoidance for multithreaded programsc In USENIX Symposium on Operating Systems Design and Implementation, 2008. [2] Wang, Y., Kelly, T., Kudlur, M., Mahlke, S., and Lafortune, S., vThe application of supervisory control to deadlock avoidance in concurrent software “ In International Workshop on Discrete Event Systems, 2008. [3] T. Murata, “Petri nets: properties, analysis and application,” in Proceedings of the IEEE, vol. 77, no. 4, pp. 541–580, 1989. [4] Li, Z. W. and M. C. Zhou., ”Elementary Siphons of Petri Nets and Their Application to Deadlock Prevention in Flexible Manufacturing Systems,” IEEE Trans. Syst. Man Cybern. A., 34(1), 38-51, 2004. [5] D.Y. Chao, “Computation of elementary siphons in Petri nets for deadlock control,” Comp. J., (British Computer Society), vol. 49, no. 4, pp. 470–479, 2006. [6] D. Y. Chao, ”Improved controllability test for dependent siphons in S 3 PR based on elementary siphons,” Asian Journal of Control, vol. 12, no. 3, pp. 377 – 391, doi:10.1002/asjc.217, 2010. [7] D. Y. Chao, Jiun-Ting Chen, Mike Y.J. Lee, and Kuo-Chiang Wu, ”Controllability of Strongly and Weakly Dependent Siphons under Disturbanceless Control,” Intelligent Control and Automation, vol.2, no.4, pp. 310-319, 2011. 361
Fig. 3. Example a 3-compound weakly dependent siphon. S 0 =S 6 =S 1 ⊕S 2 ⊕S 3 , S 4 =S 1,2 , S 5 =S 2,3 and η 0 =η 1 +η 2 +η 3 -η 4 -η 5 . TABLE III EIGHT SMS S, AND CORE CIRCUITS IN FIG. 3. S Set of places c S 1 p 5 , p 17 , p 14 , p 15 , p 16 c 1 =[p 14 t 4 p 16 t 1 p 15 t 2 p 14 ] S 2 p 4 , p 26 , p 12 , p 13 , p 14 , p 15 c 2 =[p 15 t 2 p 14 t 3 p 13 t 18 p 12 t 8 p 15 ] S 3 p 2 , p 27 , p 11 , p 12 , p 13 c 3 =[p 13 t 18 p 12 t 17 p 17 t 14 p 13 ] S 4 p 4 , p 17 , p 14 , p 15 c 4 =[p 15 t 2 p 14 t 6 p 15 ]]. S 5 p 2 , p 26 , p 12 , p 13 c 5 =[p 13 t 18 p 12 t 12 p 13 ] S 6 p , p , p , p , p , p , p , p 16 5 27 11 12 13 14 15 c 6 =c 1 ⊕c 2 ⊕c 3 S 7 p 5 , p 26 , p 12 , p 13 , p 14 , p 15 , p 16 c 7 = c 1 ⊕c 2 S 8 p 4 , p 27 , p 11 , p 12 , p 13 , p 14 , p 15 c 8 = c 2 ⊕c 3 TABLE IV EIGHT SMS S,[S] AND η IN FIG. 3. S Set of places [S] η S 1 p 5 , p 17 , p 14 , p 15 , p 16 p 3 , p 4 , p 7 , p 8 , p 9 , p 10 t 1 -t 3 +t 7 -t 10 S 2 p 4 , p 26 , p 12 , p 13 , p 14 , p 15 p 2 , p 3 , p 8 , p 9 , p 10 , p 17 , p 21 , p 23 , p 24 , p 25 t 2 -t 4 +t 13 -t 17 S 3 p 2 , p 27 , p 11 , p 12 , p 13 p 20 , p 21 , p 23 , p 24 , p 25 , p 26 -t 8 +t 14 -t 16 +t 18 S 4 p 4 , p 17 , p 14 , p 15 p 3 , p 8 , p 9 , p 10 t 2 -t 3 -t 4 +t 7 S 5 p 2 , p 26 , p 12 , p 13 p 21 , p 23 , p 24 , p 25 -t 8 +t 13 -t 17 +t 18 S 6 p 5 , p 27 , p 11 , p 12 , p 13 , p 14 , p 15 , p 16 p 2 , p 3 , p 4 , p 7 , p 8 , p 9 , p 10 , p 17 , p 20 , p 21 , p 23 , p 24 , p 25 , p 26 t 1 -t 10 +t 14 -t 16 S 7 p 5 , p 26 , p 12 , p 13 , p 14 , p 15 , p 16 p 2 , p 3 , p 4 , p 7 , p 8 , p 9 , p 10 , p 17 , p 21 , p 23 , p 24 , p 25 t 1 -t 10 +t 13 -t 17 S 8 p 4 , p 27 , p 11 , p 12 , p 13 , p 14 , p 15 p 2 , p 3 , p 8 , p 9 , p 10 , p 17 , p 20 , p 21 , p 23 , p 24 , p 25 , p 26 t 2 -t 4 +t 14 -t 16 362
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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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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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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
Page 709 and 710:
Figure 1. System overview of the SD
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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
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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
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4) Associations: In the first test
Page 767 and 768:
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
Page 781 and 782:
two stages. The first stage is focu
Page 783 and 784:
754
Page 785 and 786:
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
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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