SEKE 2012 Proceedings - Knowledge Systems Institute

More documents

Recommendations

Info

are the same, i.e. social closeness is not considered, therefore the number of positive and negative examples from each teacher agent are the same. We got 21 positive examples and 21 negative examples from each teacher agent for those concepts. Those positive and negative examples are given to Ag L to learn the new concept “computer science”. We use three machine learning techniques: K-NN, Naive Bayes, SVM. TABLE I. THE SIMILARITY VALUES OF THE SEARCH RESULTS. THE SEARCH KEYWORDS ARE (“COMPUTER SCIENCE” OR “PROGRAM LANGUAGE”) University/department sim (q spec, C best) Cornell Computer science e 0.36 Michigan Electrical engineering and 0.12 computer science Washington Computer science and 0.25 engineering TABLE II. USING K-NN FOR LEARNING true CS false CS positive CS 52 23 negative CS 11 40 Overall accuracy = 73.02% TABLE III. USING NAIVE BAYES FOR LEARNING true CS false CS positive CS 42 21 negative CS 21 42 Overall accuracy = 66.71% TABLE IV. USING SVM FOR LEARNING true CS false CS positive CS 44 17 negative CS 19 46 Overall accuracy = 70.48% Where, CS is the concept “Computer Science”. In Tables II, III and IV, true CS represents the number of examples that are classified as positive examples of the CS concept; false CS represents the number of examples classified as negative examples of the CS concept; positive CS are the real positive examples of the CS concept; negative CS are the real negative examples of the CS concept. After learning the new concept “computer science”, we extract the feature vector of this concept. TF×IDF is used to extract the feature vector of each concept in all ontologies. Now the learner agent Ag L has the new concept “computer science” in its ontology. Ag L has also a feature vector of this concept (See Table V). We need to update the strength of ties between the learner agent and each teacher agent. We measure the closeness between the feature vector of the new learnt concept “computer science” in Ag L and all concepts used by teacher agents (i.e. “Computer Science e” from Ag C , “Electrical Engineering and Computer Science” from Ag M and “Computer Science and Engineering” from Ag W ). The closeness values are shown in Table VI. From these values we can notice that, the learnt concept is closest in its definition to the concept of Cornell University, the next closest to University of Washington but far from the definition of the concept used by University of Michigan. We consider these closeness values as the initial values of tie strengths of our social network. TABLE V. FEATURE VECTORS OF THE NEWLY LEARNT CONCEPT AND CHOSEN CONCEPTS FROM TEACHER AGENTS’ ONOTOLOGYIES Feature vector of the learnt concept (computer science): { program , languag , comput , system , scienc , cover , function , type , algorithm , design , learn , logic , object , analysi , compil , machin , unix , java , includ , model } computer science e (Cornell University): { program , comput , system , languag , algorithm , logic , design , scienc , cover , analysi , equat , learn , model , discuss , function , network , optim , parallel , type , applic } Electrical engineering and computer science (University of Michigan): { system , design , comput , circuit , model , analysi , program , optic , control , signal , algorithm , digit , applic , devic , network , languag , linear , perform , logic , communic } computer science and engineering (University of Washington) { comput , system , design , program , algorithm , languag , softwar , analysi , parallel , model , imag , network , logic , altern , architectur , machin , simul , databas , memori , applic } TABLE VI. THE CLOSENESS VALUES BETWEEN THE LEARNER AGENT AG L AND TEACHER AGENTS AG C,AG M,AG W Teacher agent closeness value Cornell University (Ag C) 0.490 University of Michigan (Ag M) 0.087 University of Washington (Ag W) 0.180 In order to refine the definition of the learnt concept “computer science”, we use both keywords (“computer science” or “program language”) and conceptual knowledge (the feature vector extracted) in searching for the best matched concept in teacher agents’ ontologies. The selected best concepts are the same as in the first case. The number of positive and negative examples selected are proportional to the tie strengths, so we use 31 positive examples and 31 negative examples for the concept “Computer Science e” from Ag C , 12 positive examples and 12 negative examples for the concept “Computer Science and Engineering” form Ag W and 5 positive examples and 5 negative examples for the concept “Electrical Engineering and Computer Science” from Ag M . We use those sets of positives and negative examples to teach Ag L the concept “computer science” using K-NN, Naive Bayes and SVM. TABLE VII. USING K-NN FOR LEARNING true CS false CS positive 39 9 negative 9 39 Overall accuracy = 81.21% TABLE VIII. USING NAIVE BAYES FOR LEARNING true CS false CS positive 33 12 negative 15 36 Overall accuracy = 71.79% 265
TABLE IX. USING SVM FOR LEARNING true CS false CS positive 34 13 negative 15 34 Overall accuracy = 70.83% Using K-NN as a learning technique, in the first phase of the experiment, the accuracy of the learning was 73.02%. In the second phase of the experiment it is increased to 81.21%. Using Naïve Bayes as a learning technique, in the first phase of the experiment, the accuracy of the learning was 66.71%. In the second phase of the experiment it increased to 71.79%. Using SVM as a learning technique, in the first phase of the experiment the accuracy of the learning was 70.48%. In the second phase of the experiment it increased to 70.83%. We can notice that the accuracy of the learning process improved with all three algorithms. As all parameters are the same in both phases of the experiments except for the strengths of ties between the learner agent Ag L and all teacher agents Ag C , Ag M and Ag W , we can conclude that this improvement is due to using social networks, i.e. using the tie strength, in our system. Using social networks helps the learner agent to better resolve conflicts that may occur during the learning process. According to the above tie strengths used, the learner agent Ag L is closer to Ag C , which means that Ag L depends more on Ag C in learning new concepts (i.e. the learner agent trusts more the teacher agent that control the Cornell University knowledge base). That is why it uses more examples from this teacher in the learning process. The opposite occurs with Ag M . The strength of the tie between Ag L and Ag M is the weakest, so the learner agent knows that the concept definitions in this ontology are far from its own ontology, so Ag L cannot trust Ag M much, i.e. the lowest number of examples are borrowed from this agent. That helps Ag L to improve the accuracy of the learning process. That is also reflected in the updated values of the tie strengths shown in table X. Ag L gets closer to Ag C and farer from Ag M . TABLE X. THE UPDATED TIE STRENGTH BETWEEN THE LEARNER AGENT AG L AND TEACHER AGENTS AG C,AG M,AG W Teacher agent Tie strength Cornell University (Ag C) 0.51 University of Michigan (Ag M) 0.04 University of Washington (Ag W) 0.14 VIII. CONCLUSION In this paper, we propose a new mechanism to be used in learning a new concept from multiple teacher agents each having the concept represented in slightly different way in their ontologies. This concept learning technique depends on sending positive and negative examples to identify the asked concept from teacher agents to learner agent. However, the number of positive and negative examples received from each teaching agent is decided by the strength of tie between the learner and teacher. Through a detailed experiment we showed that this improves the overall accuracy of the learning process. Although in the experiment we focused on a network of four nodes to illustrate the results, the method is general enough and can be applied to networks of large number of nodes. ACKNOWLEDGEMENT Armin Eberlein would like to acknowledge the financial contribution of the American University of Sharjah. REFERENCES [1] E. H. Durfee, V. Lesser, “Negotiating task decomposition and allocation using partial global planning,” Distributed Artificial Intelligent, Issue 2, Vol 2, pp 229-244, 1989. [2] Daconta, M.C., Obrst, L.J., Smith, K.T, “The semantic web. a guide to the future of XML,” Web Services and K nowledge Management (Indianapolis (IN), USA 2003). [3] Robert A. Hanneman, Mark Riddle, “Introduction to social networks methods,” 2005. [4] Granovetter, M., “The strength of weak ties: A network theory revisited, sociological theory,” pp. 201 – 233, 1983. [5] Lin, N., Ensel, “Social resources and strength of ties: Structural factors in occupational status attainment,” American Sociological Review, vo;. 46, pp. 393 – 405, 1981. [6] Burt, R., “Structural holes: The social structure of competition,” Harvard University Press, 1995. [7] Eric Gilbert and Karrie Karahalios, “Predicting tie strength with social network,” Proceedings of t he 27th international conference on Human factors in computing systems, Boston, MA, USA, pp. 211-220, 2009. [8] J. P. Onnela, J. Saramaki, J. Hyvonen, G. Szabo, D. Lazer, K. Kaski, J. Kertesz, A. L. Barabasi, “Structure and tie strengths in a mobile communication network,” Proceedings of the National Academy of Science of the United States of America, vol. 104, no. 18, 2007. [9] Andrea Petróczi, Tamás Nepusz and Fülöp Bazsó, “Measuring tiestrength in virtual social networks,” vol. 27, no. 2, INSNA, 2007. [10] Shimaa M. El-Sherif, Behrouz Far, Armin Eberlein, “Calculating the strength of ties of a social network in a s emantic search system using Hidden Markov Models,” International Conference on Systems, Man and Cybernetics SMC 2011. [11] Olivier Cappe, Eric Moulines and Tobias Ryden, “Inference in Hidden Markov Models”, Springer, 2007. [12] Luc Steels, “The origins of ontologies and communication conventions in Multi-Agent Systems,” Autonomous Agents and Multi-Agent Systems, I(2), pp 169–194, 1998. [13] Ignazio Palmisano, Luigi Iannone, Domenico Redavid, Gionanni, Semeraro, “Ontology alignment throug instnce negotiation: A machine learning approach,” Lecture Notes in Computer Science, Vol. 4275, pp 1058–1074, 2006. [14] Mohsen Afsharchi, Behroz H. Far, “Conceptual hierarchy learning in a Multi-Agent System,” Proceedings of the Computer Society of Iran Computer Conference (CSICC), 2006. [15] Mohsen Afsharchi, Behroz H. Far, Jorg De nzinger, “Learning nonunanimous ontology concepts to communicate with groups of agents,” IAT, Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp 211–217, 2006. [16] B. H. Far, C. Zhong, Z. Yang and M. Afsharchi, “Realization of semantic search using concept learning and document annotation agents,” The 21th Int. Conf. on S oftware Engineering and Knowledge Engineering SEKE 2009, 2009. [17] Zilan (Nancy) Yang, “A practical ontology-based concept learning in MAS,” master thesis, University of Calgary,Department of Electrical and Computer Engineering, 2010. [18] Shimaa El-Sherif, Behrouz Far and A rmin Eberlein, “Using social networking in r esolving conflicts of concept learning process,” Proceedings of IEEE CCECE, 2010. [19] Shimaa El-Sherif, Behrouz Far and Armin Eberlein,”Semantic search based on multi-agent system and social networking,” Proceedings of the sixth IASTED International Conference, ACSE, pp. 103-110, 2010. [20] Mohsen Afsharchi, Behrouz H. Far, Jörg Denzinger, “Learning nonunanimous ontology concepts to communicate with groups of agents,” IAT 2006: 211-217. 266
Page 1 and 2:
SEKE San Francisco Bay July 1-3 201
Page 3 and 4:
Copyright © 2012 by Knowledge Syst
Page 5 and 6:
The 24 th International Conference
Page 7 and 8:
Zhenyu Chen, Nanjing University, Ch
Page 9 and 10:
Riccardo Martoglia, University of M
Page 11 and 12:
Poster/Demo Sessions Co-Chairs Fars
Page 13 and 14:
Evaluating the Cost-Effectiveness o
Page 15 and 16:
Program Understanding Evaluating Op
Page 17 and 18:
An Empirical Study of Execution-Dat
Page 19 and 20:
Computer Forensics: Toward the Cons
Page 21 and 22:
Modeling Bridging KDM and ASTM for
Page 23 and 24:
A Mapping Study on Software Product
Page 25 and 26:
A proposal for the improvement of t
Page 27 and 28:
Panel on Future Trends of Software
Page 29 and 30:
een available for analysis. Social
Page 31 and 32:
The rest of this paper is orga nize
Page 33 and 34:
Now we assume is given, we first pr
Page 35 and 36:
“iphone” and “blackberry” a
Page 37 and 38:
nique estimate the impact set based
Page 39 and 40:
Figure 1. An example to illustrate
Page 41 and 42:
trategy, the results should support
Page 43 and 44:
Figure 1. System Architecture would
Page 45 and 46:
Figure 2. Sequence Diagram of Train
Page 47 and 48:
deployed on Android phone and runs
Page 49 and 50:
the ontology information is only us
Page 51 and 52:
Both kinds of axioms lead to an inh
Page 53 and 54:
engineer and achieving requirements
Page 55 and 56:
elicitation and the act ivities con
Page 57 and 58:
Step 2. Step 3. 1.2. Use of Procedu
Page 59 and 60:
Figure 6.b. Task: Cognitive Analysi
Page 61 and 62:
original User’s Discourse / Speec
Page 63 and 64:
II. RELATED WORK Business processes
Page 65 and 66:
test, we did not find any significa
Page 67 and 68:
For the indicator “requirements a
Page 69 and 70:
the model checkers SPIN [8] and NuS
Page 71 and 72:
allow for the presentation of syste
Page 73 and 74:
the requirement or design phases sh
Page 75 and 76:
using the CR estimates against the
Page 77 and 78:
Data Set TABLE II. DATA SETS Artifa
Page 79 and 80:
Figure 4. Relative Error in the Est
Page 81 and 82:
Take basic requirements from user a
Page 83 and 84:
sent Semantic Web Ontologies. It co
Page 85 and 86:
The rest of the paper is organized
Page 87 and 88:
and the risk level. Existing assess
Page 89 and 90:
diagram could give developers an in
Page 91 and 92:
Figure 2. Co
Page 93 and 94:
• Sensor(Lexical): at the top and
Page 95 and 96:
An Overview of the RSLingo Approach
Page 97 and 98:
Figure 2. Overview of the RSLingo a
Page 99 and 100:
DETECTING EMERGENT BEHAVIOR IN DIST
Page 101 and 102:
Definition 2: For a set of MSCs M,
Page 103 and 104:
Stability of Filter-Based Feature S
Page 105 and 106:
Algorithm 1: Threshold-Based Featur
Page 107 and 108:
MI KS GM AUC PRC S2N RUS35 RUS50 RO
Page 109 and 110:
80
Page 111 and 112:
82
Page 113 and 114:
84
Page 115 and 116:
86
Page 117 and 118:
Cloud Application Resource Mapping
Page 119 and 120:
Fig. 1. UML Collaboration Diagram m
Page 121 and 122:
Fig. 2. UML Activity Diagram modeli
Page 123 and 124:
An Empirical Study of Software Metr
Page 125 and 126:
TABLE I SOFTWARE DATASETS CHARACTER
Page 127 and 128:
1) Classifiers: In this study, soft
Page 129 and 130:
Progressive Clustering with Learned
Page 131 and 132:
IV. DATA SOURCES We have been worki
Page 133 and 134:
meta-data we assign to indicate sig
Page 135 and 136:
Multi-Objective Optimization of Fuz
Page 137 and 138:
uilding FNNs that satisfy different
Page 139 and 140:
VI. EXPERIMENTAL RESULTS The presen
Page 141 and 142:
Generating Performance Test Scripts
Page 143 and 144:
test duration(s) for the scenario(s
Page 145 and 146:
which are composed by the name and
Page 147 and 148:
A Catalog of Patterns for Concept L
Page 149 and 150:
assisted solution for lattice analy
Page 151 and 152:
The complete lattice of the class s
Page 153 and 154:
Client-Side Rendering Mechanism: A
Page 155 and 156:
JavaScript code directly within the
Page 157 and 158:
Fig. 6. CPU usage percentage of pre
Page 159 and 160:
may introduce the extra learning cu
Page 161 and 162:
[9] but no validation is provided i
Page 163 and 164:
One of the st udents from Group B t
Page 165 and 166:
scribed by the equation: where η i
Page 167 and 168:
4 Model Checking DPN We use HyTech
Page 169 and 170:
Recommender systems are usually cla
Page 171 and 172:
such as one week and gradually dete
Page 173 and 174:
•
Page 175 and 176:
146
Page 177 and 178:
Enforcing Contracts for Aspect-orie
Page 179 and 180:
Table 1. Behavioral Rules in LSD [9
Page 181 and 182:
1 public privileged aspect Update {
Page 183 and 184:
Towards More Generic Aspect-Oriente
Page 185 and 186:
vals must be denoted by some notion
Page 187 and 188:
Aspect-Orientation in the Developme
Page 189 and 190:
Table II SELECTED PRIMARY STUDIES #
Page 191 and 192:
Evaluating Open Source Reverse Engi
Page 193 and 194:
Table 1. Candidate reverse engineer
Page 195 and 196:
Some tools may perform the needed f
Page 197 and 198:
Coordination Model to Support Visua
Page 199 and 200:
this CMV approach we employee three
Page 201 and 202:
colored according to the Gradient T
Page 203 and 204:
Improving Program Comprehension in
Page 205 and 206:
The method used to support and eval
Page 207 and 208:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Page 209 and 210:
An Approach for Software Component
Page 211 and 212:
properties. In particular, the foll
Page 213 and 214:
Conference Dataset Anatomy Dataset
Page 215 and 216:
equipment maintenance node may cont
Page 217 and 218:
Online Anomaly Detection for Compon
Page 219 and 220:
B. Monitoring Static Method Invocat
Page 221 and 222:
undle which is responsible for pars
Page 223 and 224:
An Exploratory Study of One-Use and
Page 225 and 226:
Figure 1. Mindmap of Reuse Design P
Page 227 and 228:
subject also caused outliers for re
Page 229 and 230:
Choosing licenses in free open sour
Page 231 and 232:
The important aspect is to provide
Page 233 and 234:
that holds a model of each software
Page 235 and 236:
Online Probability Application Char
Page 237 and 238:
TABLE II THE PARAMETERS FOR THE HAR
Page 239 and 240:
[10] N. Gunther, “Hit-and-run tac
Page 241 and 242:
Section 5 d iscusses some related w
Page 243 and 244: model and the analysis results. The
Page 245 and 246: tree view of UML model, as show in
Page 247 and 248: II. BACKGROUND In this section, we
Page 249 and 250: Algorithm 2 MarkStatements function
Page 251 and 252: (a) printtok (b) printtok2 (c) sche
Page 253 and 254: Gupta extended HGS and proposed the
Page 255 and 256: manage intellectual knowledge with
Page 257 and 258: D. Manage Training When any trainin
Page 259 and 260: TABLE II. COMPARISON BETWEEN THE EX
Page 261 and 262: B. Trace Semantics of DAP Fig. 1. A
Page 263 and 264: ∀ [DR ′ ] []gen [; ] [D]□ [;
Page 265 and 266: transformation idea in MDA [13] . T
Page 267 and 268: MappingRule MappingTGtoHP { [Rule I
Page 269 and 270: executing a continuous transition,
Page 271 and 272: protocol of DATS we used is properl
Page 273 and 274: in Boolean search and reasoning has
Page 275 and 276: to be broken by expelling one of it
Page 277 and 278: where, among other things: 1. The t
Page 279 and 280: A never-ending language learner cal
Page 281 and 282: 4. i 2 Learning(DEC, mex) via Bias
Page 283 and 284: 4.4. An Illustrative Example Now le
Page 285 and 286: Evolutionary Learning and Fuzzy Log
Page 287 and 288: In addition to the rules, the knowl
Page 289 and 290: As described in the previous sectio
Page 291 and 292: the ontology hierarchy; learning a
Page 293: three different learning techniques
Page 297 and 298: Siemens set contains seven C progra
Page 299 and 300: The program tcas is not included be
Page 301 and 302: esults. To be more specific, they w
Page 303 and 304: According to IEEE [10], regression
Page 305 and 306: (a) Volatility (b) Complexity (c) R
Page 307 and 308: It mimics the test engineer knowled
Page 309 and 310: integrate the test results. • Aut
Page 311 and 312: No tify the cloud controller to get
Page 313 and 314: of execution-data classification. T
Page 315 and 316: Figure 1. Boxplots of average AUC r
Page 317 and 318: anches is more applicable than the
Page 319 and 320: To validate the proposed approach,
Page 321 and 322: contains broadcast of Subject to Ob
Page 323 and 324: S 1a=1; S 2b=5; cobegin { S 3read
Page 325 and 326: .
Page 327 and 328: 298
Page 329 and 330: A functionality is the ability of a
Page 331 and 332: level); in UML/J2EE technology [11]
Page 333 and 334: • to provide a unified catalogue
Page 335 and 336: flexibility they provide to model s
Page 337 and 338: complete in zero-time, and thus hav
Page 339 and 340: figure assumes the experiment with
Page 341 and 342: could be a practical way of dealing
Page 343 and 344: and (iv) the reduced amount of line
Page 345 and 346:
III.BUSINESS RULES FRAMEWORK FOR KN
Page 347 and 348:
explanation for why the program wan
Page 349 and 350:
320
Page 351 and 352:
322
Page 353 and 354:
A model introducing SOAs quality at
Page 355 and 356:
there exist a hierarchical ranking
Page 357 and 358:
Software as a Service: Undo Hernán
Page 359 and 360:
operations consist in reinjection i
Page 361 and 362:
stored. If event failure, external
Page 363 and 364:
ensure users the trustworthiness of
Page 365 and 366:
Patient Doctor Doctor Patient Direc
Page 367 and 368:
going down at a time. This is a rea
Page 369 and 370:
Decidability of Minimal Supports of
Page 371 and 372:
A S 1 1 1 0 0 0 1 1 0 0 0 1 1
Page 373 and 374:
satisfies R( D) R( C) S 1. Compa
Page 375 and 376:
Automated Generation of Concurrent
Page 377 and 378:
target of testing. A blackbox test
Page 379 and 380:
sequence. Each t i θ i in the firi
Page 381 and 382:
SAMAT -AToolforSoftware Architectur
Page 383 and 384:
Figure 4. The SAM Hierarchical Mode
Page 385 and 386:
High-level Petri net Place Place Ty
Page 387 and 388:
verification and thus is often limi
Page 389 and 390:
(r 1 r 2 ...r k ) 1 denotes Path [r
Page 391 and 392:
Fig. 3. Example a 3-compound weakly
Page 393 and 394:
364
Page 395 and 396:
366
Page 397 and 398:
368
Page 399 and 400:
necessary before verification. In t
Page 401 and 402:
CTL formula as: AG (( jp EX( jp ad
Page 403 and 404:
equirements level, like EA-Miner [1
Page 405 and 406:
376
Page 407 and 408:
378
Page 409 and 410:
380
Page 411 and 412:
II. RESOURCE-ORIENTED WORKFLOW MODE
Page 413 and 414:
eventually triggers a co mmon task.
Page 415 and 416:
Input: A resource oriented workflow
Page 417 and 418:
access control exists, the action i
Page 419 and 420:
(ACCDA), is customized to address a
Page 421 and 422:
ICrudSchema and shown in Figure 3.
Page 423 and 424:
Connectors for Secure Software Arch
Page 425 and 426:
Non-repudiation security service pr
Page 427 and 428:
anAsynchronousCustomer InterfaceCon
Page 429 and 430:
How Social Network APIs Have Ended
Page 431 and 432:
OSN Social Feed Unique Features Con
Page 433 and 434:
users can control the reach of thei
Page 435 and 436:
Computer Forensics: Toward the Cons
Page 437 and 438:
In 2009, Cohen et al. in [4] have o
Page 439 and 440:
dcterms:contributor, dcterms:creato
Page 441 and 442:
A Holistic Approach to Software Tra
Page 443 and 444:
Figure 1. Figure 1 shows an overvie
Page 445 and 446:
B. Case study in a proprietary proj
Page 447 and 448:
Pointcut Design with AODL Saqib iqb
Page 449 and 450:
system attributes, methods and exec
Page 451 and 452:
Feature modeling and Verification b
Page 453 and 454:
hasOptionalPart hasPart hasMandato
Page 455 and 456:
A Context Ontology Model for Pervas
Page 457 and 458:
Figure 1. UML view of the model. Th
Page 459 and 460:
Figure 3. Architecture for deliveri
Page 461 and 462:
Ontology-based Representation of Si
Page 463 and 464:
Figure 2. Upper Ontology fragment f
Page 465 and 466:
I2Sim simulation model was transfor
Page 467 and 468:
An ontology-based approach for stor
Page 469 and 470:
fying the equivalences between conc
Page 471 and 472:
• Homonymy conflicts: occur when
Page 473 and 474:
Automatic Generation of Architectur
Page 475 and 476:
we dealt w ith the verticals rules.
Page 477 and 478:
Towards Architectural Evolution thr
Page 479 and 480:
different from replacing it, we dec
Page 481 and 482:
Using FCA-based Change Impact Analy
Page 483 and 484:
Table 2. Impact set of C1,C2,C5 cha
Page 485 and 486:
Figure 3. The PF and PTS results fo
Page 487 and 488:
Forecasting Fault Events in Power D
Page 489 and 490:
which the airport data did not cont
Page 491 and 492:
classified. Recall is the probabili
Page 493 and 494:
Testing Interoperability Security P
Page 495 and 496:
Figure 2. Overview of the Test Gene
Page 497 and 498:
Objective Figure 4. Testing
Page 499 and 500:
A New Approach to Evaluate Path Fea
Page 501 and 502:
k v ( df i ) 0 , it means that the
Page 503 and 504:
Model & Method Feasibility Evaluati
Page 505 and 506:
Structural Testing for Multithreade
Page 507 and 508:
adequate to another criterion. This
Page 509 and 510:
480
Page 511 and 512:
482
Page 513 and 514:
484
Page 515 and 516:
A Tiny Specification Metalanguage W
Page 517 and 518:
C. Syntax Extensions The expressive
Page 519 and 520:
however, does not allow variables f
Page 521 and 522:
derivation dependencies. The third
Page 523 and 524:
model extension as proposed in [6].
Page 525 and 526:
Figure 1. The process for engineeri
Page 527 and 528:
Figure 4. The abstractions extracte
Page 529 and 530:
way to detect something which could
Page 531 and 532:
Algorithm 1: Algorithm to recursive
Page 533 and 534:
obtain the input of experts on desi
Page 535 and 536:
A. Pattern Reuse In patterns reuse
Page 537 and 538:
IV. SCATTER In order to enhance pat
Page 539 and 540:
These latter would be clustered acc
Page 541 and 542:
described or even r etrieved, thus
Page 543 and 544:
DC2AP Element and their Application
Page 545 and 546:
21. Consequences Example of use DC2
Page 547 and 548:
Legacy2Cloud logic. In this scope,
Page 549 and 550:
2.3.1 Complementarity of MDD techno
Page 551 and 552:
3.2.2 EAggregateRelationship The EA
Page 553 and 554:
cess. The proposal follows the ADM
Page 555 and 556:
II. OVERVIEW OF MODAL TRANSITION SY
Page 557 and 558:
(1) V □ (A) ⊆ V □ (B), V ♦
Page 559 and 560:
Suppose P = 〈S P , SP i , AI P ,
Page 561 and 562:
Adaptive software should basically
Page 563 and 564:
A. Step S1 - Define the Business Pr
Page 565 and 566:
F. Step S6 - Monitor at Run-Time Th
Page 567 and 568:
Figure 1, a metamodel defines an XM
Page 569 and 570:
Figure 3 is a feature diagram that
Page 571 and 572:
ecause the system of C-net model is
Page 573 and 574:
Pub-T Tc Tc Sub-T Sub-T Figu
Page 575 and 576:
Visual Studio Achievements, a Visua
Page 577 and 578:
the project. This achievement has t
Page 579 and 580:
proposed in this work requires the
Page 581 and 582:
FTS is an important research area,
Page 583 and 584:
C. Dependent Variables and Measures
Page 585 and 586:
D. Conclusion Validity Conclusion v
Page 587 and 588:
evaluation of team productivity, qu
Page 589 and 590:
Figure 3: Process Fragment Example
Page 591 and 592:
complexity issues traditionally inv
Page 593 and 594:
Fig. 1: Swing Framework Class Diagr
Page 595 and 596:
ize:Class”, with an empty precond
Page 597 and 598:
Fig. 11: Overwritten Method Fig. 12
Page 599 and 600:
Investigating the use of Bayesian n
Page 601 and 602:
process for the network. Thus, this
Page 603 and 604:
Reuse of Experiences Applied to Req
Page 605 and 606:
Step 1: To start the process, the u
Page 607 and 608:
Specification of Safety Critical Sy
Page 609 and 610:
current belief of agents in order t
Page 611 and 612:
Using the Results from a Systematic
Page 613 and 614:
obtained from the categorization of
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 and 622:
About the category “Improvements
Page 623 and 624:
Identification Guidelines for the D
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
show all

SEKE 2012 Proceedings - Knowledge Systems Institute

Create successful ePaper yourself

Delete template?

Save as template?