When playing games, the user expects an easy and intuitive interaction. While current controllers are physical hardware components with a default configuration of buttons, different games use different buttons and dem...
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ISBN:
(纸本)9783319245898;9783319245881
When playing games, the user expects an easy and intuitive interaction. While current controllers are physical hardware components with a default configuration of buttons, different games use different buttons and demand different interaction methods. Besides, the player style varies according to personal characteristics or past gaming experiences. In previous works we proposed a novel virtual controller based on a common touchscreen device, such as smartphone or tablet, that is used as a gamepad to control a game on a computer or game console. In this work we include machine-learning techniques for an intelligent adaption of the layout and control elements distribution, minimizing errors and providing an enjoyable experience for individual users. We also present different usability tests and show considerable improvements in the precision and game performance of the user. We expect to open a new way of designing console and desktop games, allowing game designers to project individual controllers for each game.
How best to assess the creativity of a large number of designed artifacts remains an open problem. The typical approach is to have experts answer likert questions about individual artifacts. This process typically req...
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ISBN:
(纸本)9781450335980
How best to assess the creativity of a large number of designed artifacts remains an open problem. The typical approach is to have experts answer likert questions about individual artifacts. This process typically requires a substantial amount of training to ensure the judges achieve an acceptable level of agreement. Consequently, the approach does not scale well as it is infeasible to have multiple experts regularly evaluate the creativity of a large number of designs. The current work explores an alternative approach that uses both individual and pairwise judgements from novice crowd workers to support reliable and scalable assessment of creative designs. This approach, which we call TrueCreativity, can operate over a set of evaluations from a large number of judges and appropriately weights their evaluations based on their past reliability and agreement with other judges. We show that this approach produces results that strongly correlate with another measure of creativity.
Finding the optimal number of groups in the context of a clustering algorithm is identified as a difficult problem. In this article, we automate this choice for the spectral clustering algorithm with a novel heuristic...
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ISBN:
(纸本)9781479952083
Finding the optimal number of groups in the context of a clustering algorithm is identified as a difficult problem. In this article, we automate this choice for the spectral clustering algorithm with a novel heuristic. Our method is deterministic, and remarkable by its low computational burden. We show its effectiveness with respect to the state of the art, and further investigate assumptions underlying previous work through an empirical study, with the support of synthetic and real data sets.
The time-consuming evaluation of a product's lifetime or quality often prevents manufacturers from meeting market requirements within the time allotted for product development. Degradation profiles obtained from h...
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The time-consuming evaluation of a product's lifetime or quality often prevents manufacturers from meeting market requirements within the time allotted for product development. Degradation profiles obtained from harsh testing environments have been widely used in many applications to shorten the evaluation time. In this paper, we propose a novel recursive support vector censored regression (r-SVCR) technique to make a direct prediction on the lifetime based on the degradation profiles obtained in an accelerated testing setup. The proposed approach avoids potential bias introduced in the conventional prediction models due to accumulation of computational errors and misspecification of covariate models. Compared to standard support vector regression, our r-SVCR imposes the constraints on the derivatives of the regression function to ensure that the regression function is monotone over the input data range. Also, the r-SVCR accommodates the censored observations through our developed recursive estimation procedure, leading to error reduction. The hyperparameters of the proposed method are optimized based on the genetic algorithms (GAs). The proposed method represents a novel approach in that the functional form describing the degradation paths and even the relationship between input covariates and product degradation need not be specified. A real-life example of a degradation test in which both temperature and cut-off voltage stresses are employed to expedite a secondary rechargeable battery's failure during test intervals is presented to illustrate the proposed method and compare its performance with the conventional one. The results demonstrate the efficiency of the proposed method in predicting the lifetimes from the degradation profiles.
Most existing rough set-based feature selection algorithms suffer from intensive computation of either discernibility functions or positive regions to find attribute reduct. In this paper, we develop a new computation...
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ISBN:
(纸本)3540286535
Most existing rough set-based feature selection algorithms suffer from intensive computation of either discernibility functions or positive regions to find attribute reduct. In this paper, we develop a new computation model based on relative attribute dependency that is defined as the proportion of the projection of the decision table on a subset of condition attributes to the projection of the decision table on the union of the subset of condition attributes and the set of decision attributes. To find an optimal reduct, we use information entropy conveyed by the attributes as the heuristic. A novel algorithm to find optimal reducts of condition attributes based on the relative attribute dependency is implemented using Java, and is experimented with 10 data sets from UCI machinelearning Repository. We conduct the comparison of data classification using C4.5 with the original data sets and their reducts. The experiment results demonstrate the usefulness of our algorithm.
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