Keystroke Dynamics Patterns While Writing Positive and Negative Opinions.

This paper deals with analysis of behavioural patterns in human-computer interaction. In the study, keystroke dynamics were analysed while participants were writing positive and negative opinions. A semi-experiment with 50 participants was performed. The participants were asked to recall the most negative and positive learning experiences (subject and teacher) and write an opinion about it. Keystroke dynamics were captured and over 50 diverse features were calculated and checked against the ability to differentiate positive and negative opinions. Moreover, classification of opinions was performed providing accuracy slightly above the random guess level. The second classification approach used self-report labels of pleasure and arousal and showed more accurate results. The study confirmed that it was possible to recognize positive and negative opinions from the keystroke patterns with accuracy above the random guess; however, combination with other modalities might produce more accurate results.

A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors.

In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the appropriate hardware and sufficient power to process complex data from video, audio, and other channels. However, the increase in computing and communication capabilities of smartphones, the variety of their built-in sensors, as well as the availability of cloud computing services have made them an environment in which the task of recognising emotions can be performed at least as effectively. This is possible and particularly important due to the fact that smartphones and other mobile devices have become the main computer devices used by most people. This article provides a systematic overview of publications from the last 10 years related to emotion recognition methods using smartphone sensors. The characteristics of the most important sensors in this respect are presented, and the methods applied to extract informative features on the basis of data read from these input channels. Then, various machine learning approaches implemented to recognise emotional states are described.

Automatic recognition of therapy progress among children with autism.

The article presents a research study on recognizing therapy progress among children with autism spectrum disorder. The progress is recognized on the basis of behavioural data gathered via five specially designed tablet games. Over 180 distinct parameters are calculated on the basis of raw data delivered via the game flow and tablet sensors - i.e. touch screen, accelerometer and gyroscope. The results obtained confirm the possibility of recognizing progress in particular areas of development. The recognition accuracy exceeds 80%. Moreover, the study identifies a subset of parameters which appear to be better predictors of therapy progress than others. The proposed method - consisting of data recording, parameter calculation formulas and prediction models - might be implemented in a tool to support both therapists and parents of autistic children. Such a tool might be used to monitor the course of the therapy, modify it and report its results.

Differentiating of cross-reactions in patients with latex allergy with the use of ISAC test.

INTRODUCTION: Differentiating between cross-reactivity and double sensitization is still a challenging issue in allergology. AIM: To differentiate cross-reactions accompanying latex allergy with the use of the ISAC test. MATERIAL AND METHODS: Thirty-nine patients reporting immediate allergic reactions to latex were enrolled into the study (group A). The control group was comprised of 41 patients with allergic diseases not associated with latex (group B) and 20 healthy individuals (group C). Their history was recorded and skin prick tests were performed with latex, airborne and food allergens. Specific IgE against food allergens, latex (k82) and recombined latex allergens were determined. ImmunoCAP ISAC test was performed with 103 molecules. RESULTS: Sensitization to latex was found by means of skin tests in 16 cases and sIgE against latex was revealed in 12 cases (including 10 positive in both SPT and sIgE). In the ISAC test antibodies against recombined latex allergens were found in 8 patients with rHev b 6 as the most common. All the patients positive for rHev b 1, 5, 6, 8 had allergy or asymptomatic sensitization to food allergens cross-reacting with latex. Some reactions could not have been differentiated due to the lack of allergens in the ISAC test. Others, not related to latex-fruits syndrome were explained by cross-reactivity with other profilins or PR-10 proteins. CONCLUSIONS: ImmunoCAP ISAC test could be useful in differentiating between cross-reactions and double sensitizations. However, in the case of latex its advantages are limited due to a small panel of allergens.

Fisher sequential classifiers.

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.