Brain decoding: Opportunities and challenges for pattern recognition

The neuroimaging community heavily relies on statistical inference to explain measured brain activity given the experimental paradigm. Undeniably, this method has led to many results, but it is limited by the richness of the generative models that are deployed, typically in a mass-univariate way. Such an approach is suboptimal given the high-dimensional and complex spatiotemporal correlation structure of neuroimaging data.Over the recent years, techniques from pattern recognition have brought new insights into where and how information is stored in the brain by prediction of the stimulus or state from the data. Pattern recognition is intrinsically multivariate and the underlying models are data-driven. Moreover, the predictive setting is more powerful for many applications, including clinical diagnosis and brain–computer interfacing. This special issue features a number of papers that identify and tackle remaining challenges in this field. The specific problems at hand constitute opportunities for future research in pattern recognition and neurosciences.     

A framework for generating tunable test functions for multimodal optimization

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 selection of multimodal test problems is, however, rather limited and no general framework exists. This paper describes an attempt to construct a software framework which includes a variety of easily tunable test functions. The aim is to provide a general and easily expandable environment for testing different methods of multimodal optimization. Several function families with different characteristics are included. The framework implements new parameterizable function families for generating desired landscapes. Additionally the framework implements a selection of well known test functions from the literature, which can be rotated and stretched. The software module can easily be imported to any optimization algorithm implementation compatible with the C programming language. As an application example, 8 optimization approaches are compared by their ability to locate several global optima over a set of 16 functions with different properties generated by the proposed module. The effects of function regularity, dimensionality and number of local optima on the performance of different algorithms are studied.     

Wireless Face Interface: Using voluntary gaze direction and facial muscle activations for human-computer interaction

The present aim was to investigate the functionality of a new wireless prototype called Face Interface. The prototype combines the use of voluntary gaze direction and facial muscle activations, for pointing and selecting objects on a computer screen, respectively. The subjective and objective functionality of the prototype was evaluated with a series of pointing tasks using either frowning (i.e., frowning technique) or raising the eyebrows (i.e., raising technique) as the selection technique. Pointing task times and accuracies were measured using three target diameters (i.e., 25, 30, 40 mm), seven pointing distances (i.e., 60, 120, 180, 240, 260, 450, and 520 mm), and eight pointing angles (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°). The results showed that the raising technique was faster selection technique than the frowning technique for the objects that were presented in the pointing distances from 60 mm to 260 mm. For those pointing distances the overall pointing task times were 2.4 s for the frowning technique, and 1.6 s for the raising technique. Fitts’ law computations showed that the correlations for the Fitts’ law model were r = 0.77 for the frowning technique and r = 0.51 for the raising technique. Further, the index of performance (IP) value was 1.9 bits/s for the frowning technique and 5.4 bits/s for raising the eyebrows technique. Based on the results, the prototype functioned well and was adjustable so that two different facial activations can be used in combination with gaze direction for pointing and selecting objects on a computer screen.     

Shuffle or update parallel differential evolution for large-scale optimization

This paper proposes a novel algorithm for large-scale optimization problems. The proposed algorithm, namely shuffle or update parallel differential evolution (SOUPDE) is a structured population algorithm characterized by sub-populations employing a Differential evolution logic. The sub-populations quickly exploit some areas of the decision space, thus drastically and quickly reducing the fitness value in the highly multi-variate fitness landscape. New search logics are introduced into the sub-population functioning in order to avoid a diversity loss and thus premature convergence. Two simple mechanisms have been integrated in order to pursue this aim. The first, namely shuffling, consists of randomly rearranging the individuals over the sub-populations. The second consists of updating all the scale factors of the sub-populations. The proposed algorithm has been run on a set of various test problems for five levels of dimensionality and then compared with three popular meta-heuristics. Rigorous statistical and scalability analyses are reported in this article. Numerical results show that the proposed approach significantly outperforms the meta-heuristics considered in the benchmark and has a good performance despite the high dimensionality of the problems. The proposed algorithm balances well between exploitation and exploration and succeeds to have a good performance over the various dimensionality values and test problems present in the benchmark. It succeeds at outperforming the reference algorithms considered in this study. In addition, the scalability analysis proves that with respect to a standard Differential Evolution, the proposed SOUPDE algorithm enhances its performance while the dimensionality grows.     

Sixth graders’ evaluation strategies when reading Internet search results: an eye-tracking study

Eye-tracking technology was used to examine Internet search result evaluation strategies adopted by sixth-grade students (N?=?36) during ten experimental information search tasks. The relevancy of the search result’s title, URL, and snippet components was manipulated and selection of search results as well as looking into probabilities on the search result components was analysed. The results revealed that during first-pass inspection, students read the search engine page by first looking at the title of a search result. If the title was relevant, the probability of looking at the snippet of the search result increased. During second-pass inspection, there was a high probability of students focusing on the most promising search result by inspecting all of its components before making their selection. A cluster analysis revealed three viewing strategies: half of the students looked mainly at the titles and snippets; one-third with high probability examined all components; and one-sixth mainly focused on titles, leading to more frequent errors in search result selection. The results indicate that students generally made a flexible use of both eliminative and confirmatory evaluation strategies when reading Internet search results, while some seemed to not pay attention to snippet and URL components of the search results.     

An Experimental Study on the Concentration and Shape of Dents Caused by Spherical Metallic Particles in EHL Contacts©

An experimental study of metallic contaminant effects on surface indentation in EHL contacts is presented. Particles are initially spherical and are composed of M50 high-carbon steel powder. Their diameter ranges from 32 to 40 μm. An original lubrication system with a controlled level of contamination was built. The contaminant distribution and concentration are measured on-line by an automatic particle counter. Tests are conducted on a two-disk machine with different operating conditions. Particles may travel through the EHL contact only one time, the lubricant flow being used only once. The oil is a synthetic one qualified under the MIL-L-23699 specification. An optical profilometer is used to describe the indent topography and a CCD video camera to count the number of dents.

The test bench is described and the experimental procedure is presented. Specific tests were performed to quality the contamination bench. The combined effects of particles concentration and test duration on dent distribution were studied. Some results on the shape and concentration of indents versus operating conditions are presented. It is shown that over the range of test conditions considered, the number of indents on the raceways can be estimated from the particle concentration in the oil bulk. This leads to the conclusion that the particle entry ratio is close to one, i.e., the concentration of particles inside the EHL contact is close to those in the bulk.