A detailed investigation of classification methods for vowel speech imagery recognition

Accurate and fast decoding of speech imagery from electroencephalographic (EEG) data could serve as a basis for a new generation of brain computer interfaces (BCIs), more portable and easier to use. However, decoding of speech imagery from EEG is a hard problem due to many factors. In this paper we focus on the analysis of the classification step of speech imagery decoding for a three-class vowel speech imagery recognition problem. We empirically show that different classification subtasks may require different classifiers for accurately decoding and obtain a classification accuracy that improves the best results previously published. We further investigate the relationship between the classifiers and different sets of features selected by the common spatial patterns method. Our results indicate that further improvement on BCIs based on speech imagery could be achieved by carefully selecting an appropriate combination of classifiers for the subtasks involved

On the human evaluation of universal audio adversarial perturbations

[EN] Human-machine interaction is increasingly dependent on speech communication, mainly due to the remarkable performance of Machine Learning models in speech recognition tasks. However, these models can be fooled by adversarial examples, which are inputs in-tentionally perturbed to produce a wrong prediction without the changes being noticeable to humans. While much research has focused on developing new techniques to generate adversarial perturbations, less attention has been given to aspects that determine whether and how the perturbations are noticed by humans. This question is relevant since high fool-ing rates of proposed adversarial perturbation strategies are only valuable if the perturba-tions are not detectable. In this paper we investigate to which extent the distortion metrics proposed in the literature for audio adversarial examples, and which are commonly applied to evaluate the effectiveness of methods for generating these attacks, are a reliable mea-sure of the human perception of the perturbations. Using an analytical framework, and an experiment in which 36 subjects evaluate audio adversarial examples according to different factors, we demonstrate that the metrics employed by convention are not a reliable measure of the perceptual similarity of adversarial examples in the audio domain.This work was supported by the Basque Government (PRE_2019_1_0128 predoctoral grant, IT1244-19 and project KK-2020/00049 through the ELKARTEK program); the Spanish Ministry of Economy and Competitiveness MINECO (projects TIN2016-78365-R and PID2019-104966GB-I00); and the Spanish Ministry of Science, Innovation and Universities (FPU19/03231 predoctoral grant). The authors would also like to thank to the Intelligent Systems Group (University of the Basque Country UPV/EHU, Spain) for providing the computational resources needed to develop the project, as well as to all the participants that took part in the experiments

Evaluation of the Temperature and Time in Centrifugation-Assisted Freeze Concentration

Centrifugation is a technique applied to assist in the freeze concentration of fruit juices and solutions. The aim of this work was to study the influence of the time–temperature parameters on the centrifugation process as a technique applied to assist in the first cycle of the freeze concentration of blueberry juice. A completely randomized 4 × 3 factorial design was performed using temperature and time as the factors, and the response variables included the percentage of concentrate, efficiency and solutes recovered. The results were evaluated using multiple linear regression, random forest regression, and Gaussian processes. The solid content in the concentrate doubled compared to the initial sample (18 °Brix) and approached 60% in the first cycle of blueberry juice freeze concentration. The combination of factors affected the percentage of the concentrate and solutes recovered, and the optimum of concentration was obtained at 15 °C with a centrifugation time of 20 min. Gaussian processes are suggested as suitable machine learning techniques for modelling the quantitative effect of the relevant factors in the centrifugation process.Jorge Moreno and Ricardo Simpson are grateful for the financial support provided by FONDECYT 1160761. Roberto Santana acknowledges support by the Spanish Ministry of Science and Innovation (projects TIN2016-78365-R and PID2019-104966GB-I00), and the Basque Government (projects KK-2020/00049 and IT1244-19, and ELKARTEK program)

Analysis of dominant classes in universal adversarial perturbations

The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many different strategies can be employed to efficiently generate adversarial attacks, some of them relying on different theoretical justifications. Among these strategies, universal (input-agnostic) perturbations are of particular interest, due to their capability to fool a network independently of the input in which the perturbation is applied. In this work, we investigate an intriguing phenomenon of universal perturbations, which has been reported previously in the literature, yet without a proven justification: universal perturbations change the predicted classes for most inputs into one particular (dominant) class, even if this behavior is not specified during the creation of the perturbation. In order to justify the cause of this phenomenon, we propose a number of hypotheses and experimentally test them using a speech command classification problem in the audio domain as a testbed. Our analyses reveal interesting properties of universal perturbations, suggest new methods to generate such attacks and provide an explanation of dominant classes, under both a geometric and a data-feature perspective.This work is supported by the Basque Government, Spain (BERC 2018–2021 program, project KK-2020/00049 through the ELKARTEK program, IT1244-19, and PRE_2019_1_0128 predoctoral grant), by the Spanish Ministry of Economy and Competitiveness MINECO, Spain (projects TIN2016-78365-R and PID2019-104966GB-I00) and by the Spanish Ministry of Science, Innovation and Universities, Spain (FPU19/03231 predoctoral grant). Jose A. Lozano acknowledges support by the Spanish Ministry of Science, Innovation and Universities, Spain through BCAM Severo Ochoa accreditation (SEV-2017-0718)

New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination

Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAs’ convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete functions

Transfer learning in hierarchical dialogue topic classification with neural networks

Knowledge transfer between tasks can significantly improve the efficiency of machine learning algorithms. In supervised natural language understanding problems, this sort of improvement is critical since the availability of labelled data is usually scarce. In this paper we address the question of transfer learning between related topic classification tasks. A characteristic of our problem is that the tasks have a hierarchical relationship. Therefore, we introduce and validate how to implement the transfer exploiting this hierarchical structure. Our results for a real-world topic classification task show that the transfer can produce improvements in the behavior of the classifiers for some particular problems.The research presented in this paper is conducted as part of the project EM-PATHIC that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769872

Using network mesures to test evolved NK-landscapes

In this paper we empirically investigate which are the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms. To this end, we evolve instances that maximize the estimation of distribution algorithm complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K

Evolving Gaussian process kernels from elementary mathematical expressions for time series extrapolation

[EN]Choosing the best kernel is crucial in many Machine Learning applications. Gaussian Processes are a state-of-the-art technique for regression and classification that heavily relies on a kernel function. However, in the Gaussian Processes literature, kernels have usually been either ad hoc designed, selected from a predefined set, or searched for in a space of compositions of kernels which have been defined a priori. In this paper, we propose a Genetic Programming algorithm that represents a kernel function as a tree of elementary mathematical expressions. By means of this representation, a wider set of kernels can be modeled, where potentially better solutions can be found, although new challenges also arise. The proposed algorithm is able to overcome these difficulties and find kernels that accurately model the characteristics of the data. This method has been tested in several real-world time series extrapolation problems, improving the state-of-the-art results while reducing the complexity of the kernels.This work has been supported by the Spanish Ministry of Science and Innovation (project PID2019-104966 GB-I00) , and the Basque Government (projects KK-2020/00049 and IT1244-19, and ELKARTEK program) . Jose A. Lozano is also supported by BERC 2018-2021 (Basque government) and BCAM Severo Ochoa accred-itation SEV-2017-0718 (Spanish Ministry of Science and Innovation)

A quantitative analysis of estimation of distribution algorithms based on Bayesian networks

The successful application of estimation of distribution algorithms (EDAs) to solve different kinds of problems has reinforced their candidature as promising black-box optimization tools. However, their internal behavior is still not completely understood and therefore it is necessary to work in this direction in order to advance their development. This paper presents a new methodology of analysis which provides new information about the behavior of EDAs by quantitatively analyzing the probabilistic models learned during the search. We particularly focus on calculating the probabilities of the optimal solutions, the most probable solution given by the model and the best individual of the population at each step of the algorithm. We carry out the analysis by optimizing functions of different nature such as Trap5, two variants of Ising spin glass and Max-SAT. By using different structures in the probabilistic models, we also analyze the influence of the structural model accuracy in the quantitative behavior of EDAs. In addition, the objective function values of our analyzed key solutions are contrasted with their probability values in order to study the connection between function and probabilistic models. The results not only show information about the EDA behavior, but also about the quality of the optimization process and setup of the parameters, the relationship between the probabilistic model and the fitness function, and even about the problem itself. Furthermore, the results allow us to discover common patterns of behavior in EDAs and propose new ideas in the development of this type of algorithms

On the application of estimation of distribution algorithms to multi-marker tagging SNP selection

This paper presents an algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs) using an estimation of distribution algorithm (EDA). The EDA stochastically searches the constrained space of possible feasible solutions and takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions. The algorithm is evaluated across the HapMap reference panel data sets. The introduced algorithm is effective for the identification of minimal multi-marker SNP sets, which considerably reduce the dimension of the tagging SNP set in comparison with single-marker sets. New reduced tagging sets are obtained for all the HapMap SNP regions considered. We also show that the information extracted from the interaction graph representing the correlations between the SNPs can help to improve the efficiency of the optimization algorithm. keywords: SNPs, tagging SNP selection, multi-marker selection, estimation of distribution algorithms, HapMap