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Depok, Indonesia

Witt A.,University of Burgundy | Puspitawati I.,Gunadarma University | Vinter A.,University of Burgundy

Typically developing children aged 5 to 8 years were exposed to artificial grammar learning. Following an implicit exposure phase, half of the participants received neutral instructions at test while the other half received instructions making a direct, explicit reference to the training phase. We first aimed to assess whether implicit learning operated in the two test conditions. We then evaluated the differential impact of age on learning performances as a function of test instructions. The results showed that performance did not vary as a function of age in the implicit instructions condition, while age effects emerged when explicit instructions were employed at test. However, performance was affected differently by age and the instructions given at test, depending on whether the implicit learning of short or long units was assessed. These results suggest that the claim that the implicit learning process is independent of age needs to be revised. © 2013 Witt et al. Source

Puspitawati I.,University of Burgundy | Puspitawati I.,Gunadarma University | Jebrane A.,University of Burgundy | Vinter A.,University of Burgundy
Child Development

This study investigated the spatial analysis of tactile hierarchical patterns in 110 early-blind children aged 6-8 to 16-18 years, as compared to 90 blindfolded sighted children, in a naming and haptic drawing task. The results revealed that regardless of visual status, young children predominantly produced local responses in both tasks, whereas the production of integrated responses emerged later. Development of local and global processing seems to proceed similarly in the two populations, but local processing continued to occur at high levels over a larger age range in the blind. The possibility of visual mediation is pointed out, as totally blind children tended to process information locally more often than blind children with minimal light perception. © 2013 Society for Research in Child Development, Inc. Source

Rustam Z.,University of Indonesia | Talita A.S.,Gunadarma University
Journal of Theoretical and Applied Information Technology

Intrusion Detection Systems (IDS) are used as security management systems. There are two approaches of IDS, Misuse Detection (knowledge-based intrusion detection) and Anomaly Detection (behavior-based intrusion detection). Misuse detection is performed by monitoring activities which is suspected as an intrusion based on prior information about specific attacks. While anomaly detection is based on the observation of the activity that is incompatible with the acceptable behaviors in normal conditions and makes it possible to determine new type of attacks in the system. Some Computational Intelligence models have been developed to solve Intrusion Detection Systems problems such as Neural Network and NeuroFuzzy methods. They are chosen because IDS involves large data sets with several different features that can bring out negative effects on IDS accuracy and its computational time. Naïve Bayes, Decision Tree (C4.5) and Kernel Matrix Methods can be used to reduce the number of features at data sets. We propose Fuzzy Kernel C-Means Algorithm as another method to solve IDS problems that we claim provides better results while combined with Kernel Matrix method to reduce the number of selected data features. © 2005 - 2015 JATIT & LLS. Source

Rustam Z.,University of Indonesia | Talita A.S.,Gunadarma University
Journal of Theoretical and Applied Information Technology

The success of the classification method is highly dependent on how to specify initial data as the initial prototype, dissimilarity functions that we used and the presence of outliers among the data. To overcome these obstacles, in this paper we present Fuzzy Kernel k-Medoids (FKkM) algorithm that we claim to be robust against outliers, invariant under translation and data transformation, as the combined development of Fuzzy LVQ, Fuzzy k-Medoids and Kernel Function. Based on the experiments, it provides a better accuracy than Support Vector Machines, Kernel Fisher Discriminant and RBF Neural Network for multiclass multidimensional data classification. © 2005 - 2015 JATIT & LLS. All rights reserved. Source

In this paper molecular dynamics computer simulations is used to investigate a critical temperature Tc for a dynamical glass transition as proposed by the mode-coupling theory (MCT) of dense liquids in a glass forming Ni0.8Zr0.2-system. The critical temperature Tc are analyzed from different quantities and checked the consistency of the estimated values, i.e. from (i) the non-vanishing nonergodicity parameters as asymptotic solutions of the MCT equations in the arrested state, (ii) the gm-parameters describing the approach of the melt towards the arrested state on the ergodic side, (iii) the diffusion coefficients in the melt. The resulting Tc values are found to agree within about 10-15 %. © Owned by the authors, published by EDP Sciences, 2014. Source

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