http://ieeexplore.ieee.org TOC Alert for Publication# 3468 2009 November 19 39 6 C1 1141 45 39 6 C2 C2 38 39 6 1142 1151 399 39 6 1152 1164 1169 entertainment modeling). Two of the approaches are derived from the literature-the large-margin algorithm (LMA) and preference learning with Gaussian processes-while the remaining two are custom-designed approaches for the problem under investigation: meta-LMA and neuroevolution. Preference learning techniques are combined with feature set selection methods permitting the construction of effective preference models, given suitable individual playing features. The underlying preference model that best reflects children preferences is obtained through neuroevolution: 82.22% of cross-validation accuracy in predicting reported entertainment in the main set of game survey experimentation. The model is able to correctly match expressed preferences in 66.66% of cases on previously unseen data (p -value = 0.0136) of a second physical activity control experiment. Results indicate the benefit of the use of neuroevolution and sequential forward selection for the investigated complex case study of cognitive modeling in physical games.]]> 39 6 1165 1175 880 39 6 1176 1187 1142 39 6 1188 1199 782 application cards, placed in network nodes. The sharp separation of applications and network is smoothed, and by performing part of the distributed application inside the network, it is possible to reduce costs and improve performance with a better optimization of the whole distributed information and communication technology (ICT) infrastructure. This optimization is allowed by the additional degrees of freedom of placing cards in nodes and of assigning applications to such cards. In this paper, we provide an optimization algorithm that minimizes the total cost of the entire distributed ICT infrastructure, given a target performance objective defined as the end-to-end delay for the completion of the distributed application tasks. We focus on two sample applications that are well suited for application- and context-aware infrastructures: caching and protocol translation. The joint optimization of computing and communication resources is an innovative contribution of this paper, as, in the literature, hardware and network components are typically optimized separately. Results show that the total infrastructural cost savings are in the range of 15%-20%. However, savings can be obtained only if cards satisfy a cost-performance curve that is also analyzed.]]> 39 6 1200 1213 388 39 6 1214 1223 309 39 6 1224 1236 740 39 6 1237 1246 281 39 6 1247 1254 407 39 6 1255 1270 2553 39 6 1271 1282 831 class-imbalance problem that plagues so many other application domains. The majority of faults in a software system, particularly high-assurance systems, usually lie in a very small percentage of the software modules. This imbalance between the number of fault-prone (fp) and non-fp (nfp) modules can have a severely negative impact on a data-mining technique's ability to differentiate between the two. This paper addresses the class-imbalance problem as it pertains to the domain of software-quality prediction. We present a comprehensive empirical study examining two different methodologies, data sampling and boosting, for improving the performance of decision-tree models designed to identify fp software modules. This paper applies five data-sampling techniques and boosting to 15 software-quality data sets of different sizes and levels of imbalance. Nearly 50 000 models were built for the experiments contained in this paper. Our results show that while data-sampling techniques are very effective in improving the performance of such models, boosting almost always outperforms even the best data-sampling techniques. This significant result, which, to our knowledge, has not been previously reported, has important consequences for practitioners developing software-quality classification models.]]> 39 6 1283 1294 359 39 6 1295 1306 434 39 6 1307 1315 808 39 6 1316 1330 1677 39 6 1331 1338 274 39 6 1339 1347 410 3PRs is proposed, which avoids complete siphon enumerationrdquo. However, this is incorrect because Proposition 1 and Corollary 6 of the aforementioned paper, which lead to the claim, are both incorrect. In this correspondence paper, Proposition 1 and Corollary 6 are disproved. As a consequence, some claims of the aforesaid paper are denied. Two examples are presented to disprove some claims of the previously mentioned paper.]]> 39 6 1348 1350 121 39 6 1351 1351 142 39 6 1352 1352 122 39 6 1353 1368 158 39 6 C3 C3 29 39 6 C4 C4 35