Fuzzy kernel feature selection with multi-objective differential evolution algorithm Connect. Sci. (IF 0.673) Pub Date : 2019-07-09 Emrah Hancer
In this paper, we propose a multi-objective differential evolution-based filter approach for feature selection that interconnects fuzzy- and kernel-based information theory measures to find feature subsets that are optimal responses to the targets. In contrast to the existing filter approaches using the principles of information theory and rough set theory, our approach can be applied to continuous datasets without discretisation. Moreover, our study is the first in the literature that employs fuzzy and kernel measures to form a filter criterion for feature selection, to our knowledge. We prove various favourable results using a variety of benchmark datasets and also demonstrate that our approach can better search the dimensionality space to reach maximum predictive of the response.
Solving optimal control problems of the time-delayed systems by a neural network framework Connect. Sci. (IF 0.673) Pub Date : 2019-04-17 Alireza Nazemi; Ensieh Fayyazi; Marzieh Mortezaee
A numerical method using neural networks for solving time-delayed optimal control problems is studied. The problem is first transformed into one without a time-delayed argument, using a Páde approximation. We try to approximate the solution of the Hamiltonian conditions based on the Pontryagin minimum principle (PMP). For this purpose, we introduce an error function that contains all PMP conditions. We then minimise the error function where weights and biases associated with all neurons are unknown. Substituting the optimal values of the weights and biases in the trial solutions, we obtain the optimal solution of the original problem. Several examples are given to show the efficiency of the method.
Event-triggered H∞ anti-synchronisation for delayed neural networks with discontinuous neuron activations via non-fragile control strategy Connect. Sci. (IF 0.673) Pub Date : 2019-04-15 Min Liu; Huaiqin Wu; Jinde Cao; Yanning Wang
This paper treats of the global event-triggered anti-synchronisation issue for discontinuous neural networks with the mixed time-varying delays and random feedback gain fluctuation via non-fragile control strategy. The random gain uncertainties are described by stochastic variables satisfying the Bernoulli distribution. Firstly, the novel hybrid controllers, which are composed of the non-fragile controller and the event-triggered controller, are designed. Then, based on Clarke's non-smooth analysis theory, general free-weighting matrix method, the Lyapunov-Krasovskii functional approach and Wirtinger-based multiple integral inequality analysis technology, the global event-triggered non-fragile anti-synchronisation conditions are established in terms of linear matrix inequalities (LMIs). In addition, under the considered external disturbance, the conditions with respect to the global event-triggered non-fragile H∞ anti-synchronisation are also addressed in forms of LMIs. Finally, two illustrative examples are provided to verify the effectiveness of the designed event-triggered non-fragile control scheme and the validity of theoretical results.
Exploring the usability of the text-based CAPTCHA on tablet computers Connect. Sci. (IF 0.673) Pub Date : 2019-05-07 Darko Brodić; Alessia Amelio
This paper analyses and discusses the usability aspect of the text-based CAPTCHA in terms of response time and success in solving the CAPTCHA on tablet computers. The response time is the time spent by the user to find a solution to the CAPTCHA. The analysis is separately conducted on text-based CAPTCHA with only text and numbers. Then, the results are compared and the differences in response time and success in solving the two types of CAPTCHA are underlined. This is accomplished by asking 125 Internet users to solve the text-based CAPTCHA on the tablet computer. Their gender, age, education level, Internet experience, response time and success in solving two types of text-based CAPTCHA are collected in a dataset. Then, advanced statistical analysis by association rule mining is performed. It shows the dependence of the response time and success in solving the CAPTCHA on co-occurrence of gender, age, education level and Internet experience and the strength of this dependence by support, confidence and lift measures. This study provides relevant information for designing new CAPTCHAs which may be more accustomed to specific types of Internet users.
Social coordination in toddler's word learning: interacting systems of perception and action. Connect. Sci. (IF 0.673) Pub Date : 2008-06-01 Alfredo F Pereira,Linda B Smith,Chen Yu
We measured turn-taking in terms of hand and head movements and asked if the global rhythm of the participants' body activity relates to word learning. Six dyads composed of parents and toddlers (M = 18 months) interacted in a tabletop task wearing motion-tracking sensors on their hands and head. Parents were instructed to teach the labels of 10 novel objects and the child was later tested on a name-comprehension task. Using dynamic time warping, we compared the motion data of all body-part pairs, within and between partners. For every dyad, we also computed an overall measure of the quality of the interaction, that takes into consideration the state of interaction when the parent uttered an object label and the overall smoothness of the turn-taking. The overall interaction quality measure was correlated with the total number of words learned.In particular, head movements were inversely related to other partner's hand movements, and the degree of bodily coupling of parent and toddler predicted the words that children learned during the interaction. The implications of joint body dynamics to understanding joint coordination of activity in a social interaction, its scaffolding effect on the child's learning and its use in the development of artificial systems are discussed.