Tomas Blažauskas

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Rényi and Tsallis entropy). By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE)). The values of model performance statistics (R2, adjusted R2, Mean Square Error (MSE) and standard error) also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers.

The Ways of Using Augmented Reality in Education

As the improvement in the technologies is fast and widespread, it should be used for its best. Emerging technologies like an augmented reality are used to play games when at the same time it can be the best motivating educational tool. However, augmented reality tools are not spread in schools or higher education institutions, just several apps are designed for the educational purposes. The problem is that there is no one effective model which would help to increase the efficiency of the learning process and the gap between the need of different types of using augmented reality (AR) in education and different subjects is still existing. The authors provide the model of AR for the effective learning processes.

The Mobile Application Based on Augmented Reality for Learning STEM Subjects

App store is full of programs, which are based on augmented reality. A lot of studies showed that augmented reality has a lot of benefits for user’s ability to learn new things or their increase in motivation. However, the majority of these programs are dedicated for entertainment and just several of them are designed for learning processes. The authors have developed an app based on an augmented reality, which aims to provide scientific formulas for math, physics and chemistry subjects and by this to ease the exercise solving. The target group is K12 learners in school. The app was uploaded to the app store for both, iOS and Android operating systems. Further researches have to be done on finding the impact of this type of programs. If the impact would be positive, the authors suggest improving an app by adding more specific topics.

Depression Behavior Detection Model Based on Participation in Serious Games

Managing depression is one of the main challenges that health specialists have to deal with. Due to the cumulative nature of depression, the major problem is that a long-term observation of symptoms is required to make an accurate decision about an individual’s state. The depressed mood rate of an individual can be estimated according to recorded physiological and emotional information. We propose a mobile health monitoring system using wearable smart identification sensors (EEG, ECG, EMG, gaze tracking, and physical activity data) that capture stress and specific subject behaviors as a result of the participation in a serious game. The main objective is to study the impact of serious games on the human cognitive system in treating the early signs of depression by using a multi-level systems approach for representing the structure and dynamics of human cognitive functions. Our initial findings show that subjects with negative moods have been characterized by psychomotor retardation and lower correlation between the neural and cardiac systems.