Multimodal interfaces offer its users the possibility of interacting with computers, in a transparent, natural way, by means of various modalities. Fusion engines are key components in multimodal systems, responsible for combining information from different sources and extract a semantic meaning from them. This fusion process allows many modalities to be effectively used at once and therefore a...

Our group is interested in creating human machine interfaces which use natural modalities such as vision and speech to sense and interpret a user's actions [6]. In this paper we describe recent work on multimodal adaptive interfaces which combine automatic speech recognition, computer vision for gesture tracking, and machine learning techniques. Speech is the primary mode of communication betwe...

Differential Evolution (DE) is a widely used successful evolutionary algorithm (EA) based on a population of individuals, which is especially well suited to solve problems that have non-linear, multimodal cost functions. However, for a given population, the set of possible new populations is finite and a true subset of the cost function domain. Furthermore, the update formula of DE does not use...

This work develops a new functionality in UCODE_2014 to evaluate Bayesian credible intervals using the Markov Chain Monte Carlo (MCMC) method. The MCMC capability in UCODE_2014 is based on the FORTRAN version of the differential evolution adaptive Metropolis (DREAM) algorithm of Vrugt et al. (2009), which estimates the posterior probability density function of model parameters in highdimensiona...

We revisit a class of multimodal function optimizations using evolutionary algorithms reformulated into a multiobjective framework where previous implementations have needed niching/sharing to ensure diversity. In this paper, we use a steady-state multiobjective algorithm which preserves diversity without niching to produce diverse sampling of the Pareto-front with significantly lower computati...

In a multimodal conversation, the way users communicate with a system depends on the available interaction channels and the situated context (e.g., conversation focus, visual feedback). These dependencies form a rich set of constraints from various perspectives such as temporal alignments between different modalities, coherence of conversation, and the domain semantics. There is strong evidence...

Multimodal function optimization, where the aim is to locate more than one solution, has attracted growing interest especially in the evolutionary computing research community. To evaluate experimentally the strengths and weaknesses of multimodal optimization algorithms, it is important to use test functions representing different characteristics and various levels of difficulty. The available ...

Premature convergence when solving multimodal problems is still the main limitation which affects the performance of the PSO. To avoid of premature, an improved PSO algorithm with an adaptive updating mechanism (IPSO) is proposed in this paper. When the algorithm converges to a local optimum, the updating mechanism begins to work so that the stagnated algorithm obtains energy for optimization. ...