Recently, real-time facial expression recognition has attracted more and more research. In this study, an automatic facial expression real-time system was built and tested. Firstly, the system and model were designed and tested on a MATLAB environment followed by a MATLAB Simulink environment that is capable of recognizing continuous facial expressions in real-time with a rate of 1 frame per second and that is implemented on a desktop PC. They have been evaluated in a public dataset, and the experimental results were promising. The dataset and labels used in this study were made from videos, which were recorded twice from five participants while watching a video. Secondly, in order to implement in real-time at a faster frame rate, the facial expression recognition system was built on the field-programmable gate array (FPGA). The camera sensor used in this work was a Digilent VmodCAM — stereo camera module. The model was built on the Atlys™ Spartan-6 FPGA development board. It can continuously perform emotional state recognition in real-time at a frame rate of 30. A graphical user interface was designed to display the participant’s video in real-time and two-dimensional predict labels of the emotion at the same time.

An efficient acoustic events detection system EAR-TUKE is presented in this paper. The system is capable of processing continuous input audio stream in order to detect potentially dangerous acoustic events, specifically gunshots or breaking glass. The system is programmed entirely in C++ language (core math. functions in C) and was designed to be self sufficient without requiring additional dependencies. In the design and development process the main focus was put on easy support of new acoustic events detection, low memory profile, low computational requirements to operate on devices with low resources, and on long-term operation and continuous input stream monitoring without any maintenance. In order to satisfy these requirements on the system, EAR-TUKE is based on a custom approach to detection and classification of acoustic events. The system is using acoustic models of events based on Hidden Markov Models (HMMs) and a modified Viterbi decoding process with an additional module to allow continuous monitoring. These features in combination with Weighted Finite-State Transducers (WFSTs) for the search network representation fulfill the easy extensibility requirement. Extraction algorithms for Mel-Frequency Cepstral Coefficients (MFCC), Frequency Bank Coefficients (FBANK) and Mel-Spectral Coefficients (MELSPEC) are also included in the preprocessing part. The system contains Cepstral Mean Normalization (CMN) and our proposed removal of basic coefficients from feature vectors to increase robustness. This paper also presents the development process and results evaluating the final design of the system.

With the ever-progressing development in the field of computational and analytical science the last decade has seen a big improvement in the accuracy of electroencephalography (EEG) technology. Studies try to examine possibilities to use high dimensional EEG data as a source for Brain to Computer Interface. Applications of EEG Brain to computer interface vary from emotion recognition, simple computer/device control, speech recognition up to Intelligent Prosthesis. Our research presented in this paper was focused on the study of the problematic speech activity detection using EEG data. The novel approach used in this research involved the use visual stimuli, such as reading and colour naming, and signals of speech activity detectable by EEG technology. Our proposed solution is based on a shallow Feed-Forward Artificial Neural Network with only 100 hidden neurons. Standard features such as signal energy, standard deviation, RMS, skewness, kurtosis were calculated from the original signal from 16 EEG electrodes. The novel approach in the field of Brain to computer interface applications was utilised to calculated additional set of features from the minimum phase signal. Our experimental results demonstrated F1 score of 86.80% and 83.69% speech detection accuracy based on the analysis of EEG signal from single subject and cross-subject models respectively. The importance of these results lies in the novel utilisation of the mobile device to record the nerve signals which can serve as the stepping stone for the transfer of Brain to computer interface technology from technology from a controlled environment to the real-life conditions.

SK-QuAD is the first manually annotated dataset of questions and answers in Slovak. It consists of more than 91k factual questions and answers from various fields. Each question has an answer marked in the corresponding paragraph. It also contains negative examples in the form of "unanswered questions" and "plausible answers". The dataset is published free of charge for scientific use. We aim to contribute to the creation of Slovak or multilingual systems for generating an answer to a question in a natural language. The paper provides an overview of the existing datasets for question answering. It describes the annotation process and statistically analyzes the created content. The dataset expands the possibilities of training and evaluation of multilingual language models. Experiments show that the dataset achieves state-of-the-art results for Slovak and improves question answering for other languages in zero-shot learning. We compare the effect of machine-translated data with manually annotated. Additional data improve the modeling for low-resourced languages.

From the last decade, researches on human facial emotion recognition disclosed that computing models built on regression modelling can produce applicable performance. However, many systems need extensive computing power to be run that prevents its wide applications such as robots and smart devices. In this proposed system, a real-time automatic facial expression system was designed, implemented and tested on an embedded device such as FPGA that can be a first step for a specific facial expression recognition chip for a social robot. The system was built and simulated in MATLAB and then was built on FPGA and it can carry out real time continuously emotional state recognition at 30 fps with 47.44% accuracy. The proposed graphic user interface is able to display the participant video and two dimensional predict labels of the emotion in real time together.