Collecting data using a UAV nomadic gateway, which serves as a "go-between" edge device between resource-limited end sensor devices and a cloud instance may lead to data quality issues. Power outages on end devices together with MCU lacking the system clock may not only make collected time series data incomplete but also disrupt timestamping of sequences of samples. The proposed data fusion method performed at the cloud side combines information from a number of sensors to align the sequences along time.

The article describes the idea of detecting stress among programmers on the basis of keystroke dynamics.An experiment with a group of students of artificial intelligence classes was performed.Two samples of keystroke data were recorded for each case, the first while programming without stress, the second under time pressure.A number of timing and frequency parameters were calculated for each sample.Then statistical analysis was performed to evaluate the significance of keystroke parameters changes.It turned out that some of the defined features might be indicators of being stressed.

The paper regards supporting behavioral therapy of autistic children with mobile applications, specifically applied for measuring the child's progress.A family of five applications is presented, that was developed as an investigation tool within the project aimed at automation of therapy progress monitoring.The applications were already tested with children with autism spectrum disorder.Hereby we analyse children' experience with the games, as a positive attitude towards the application is the key factor enabling practical application of the solutions in therapy.The study might be interesting for both researchers and practitioners applying e-technologies in autistics therapy.

The paper concerns accuracy of emotion recognition from facial expressions. As there are a couple of ready off-the-shelf solutions available in the market today, this study aims at practical evaluation of selected solutions in order to provide some insight into what potential buyers might expect. Two solutions were compared: FaceReader by Noldus and Xpress Engine by QuantumLab. The performed evaluation revealed that the recognition accuracies differ for photo and video input data and therefore solutions should be matched to the specificity of the application domain.

The article describes a research on recognizing emotional states on the basis of keystroke dynamics. An overview of various studies and applications of emotion recognition based on data coming from keyboard is presented. Then, the idea of an experiment is presented, i.e. the way of collecting and labeling training data, extracting features and finally training classifiers. Different classification approaches are proposed to be tested: universal vs. individual models, multiclass vs. two-class. The obtained results reveal which of these approaches are appropriate for the given task. The individual two-class models turn out to be the most accurate.

The paper describes the approach based on using standard input devices, such as keyboard and mouse, as sources of data for the recognition of users' emotional states. A number of systems applying this idea have been presented focusing on three categories of research problems, i.e. collecting and labeling training data, extracting features and training classifiers of emotions. Moreover the advantages and examples of combining standard input devices with other sources of information on human emotions have been also described. Eventually some conclusions from the review have been drawn.

In this paper a novel application of multimodal emotion recognition algorithms in software engineering is described. Several application scenarios are proposed concerning program usability testing and software process improvement. Also a set of emotional states relevant in that application area is identified. The multimodal emotion recognition method that integrates video and depth channels, physiological signals and input devices usage patterns is proposed and some preliminary results on learning set creation are described.

This paper presents further discussion and development of the two-parameter Fisher criterion and describes its two modifications (weighted criterion and another multiclass form). The criteria are applied in two algorithms for training linear sequential classifiers. The main idea of the first algorithm is separating the outermost class from the others. The second algorithm, which is a generalization of the first one, is based on the idea of linear division of classes into two subsets. As linear division of classes is not always satisfactory, a piecewise-linear version of the sequential algorithm is proposed as well. The computational complexity of different algorithms is analyzed. All methods are verified on artificial and real-life data sets.