Self-voice plays an important role in our everyday lives. Apart from being used for communication, our own voice defines our identity, as it is the sound that is most intimately related to ourselves. Disturbances in self-voice recognition have been related to certain psychotic symptoms such as auditory-verbal hallucinations, colloquially called ‘hearing voices’, and to a distorted sense of self more generally. Recent research has indicated that a specific self-other voice discrimination procedure combining psychophysics, voice morphing technology, as well as air-and bone-conducted voice stimuli can be of clinical significance as a biomarker for detecting pathological alterations in self-consciousness. However, there are several limitations with the existing approach, mainly in not being simple to use and difficult to apply in different clinical contexts. To address these drawbacks, we adapted the current methodology and developed a self-other voice discrimination solution with a graphical user interface, automated voice morphing with personalized voice selection tool, and results visualization. This improves the usability of the task, shortens the procedure duration, and provides a patient-tailored approach. This paper demonstrates the technological advantages and scientific potential of the new methodology, with sample data from two different participant groups in clinical and non-clinical settings.

Controlled energy distribution and energy efficiency in buildings are among the main concerns in modern constructions, energy management and buildings usability. Total energy performance of a building depends on several factors, which include materials and components, building environment, but also the occupants behavior. User interaction with windows and window status as open or closed is the main cause of the difference between the predicted and the actual energy consumption. Understanding these user habits helps to better predict energy disruptions and enables planning for more efficient energy distribution. This paper investigates the possibility of using a machine learning model to identify the user behavior through a set of available historical data in a living-lab smart building. The random forest algorithm used in this research proved promising for the particular use case and makes a good ground for future work with window status classification accuracy of 87.85%.

Due to shopping trends change, retailers are prompt to optimize their business processes in order to provide more personalized, faster and smarter user experience, grow revenue and reduce business costs. Retail operational decisions include product allocation, product replenishment points and vehicle routes for inventory renewal. In all of these areas, a distinctive contribution lies in accurate estimation of product demand. This paper focuses on forecasting sales in retail. The product sales is modeled by using XGBoost algorithm and iterated multi-step ahead method on the horizon of 7 days. Model inputs include real historical sales data, seasonality and working/non-working day indicators. Model is tested with a real dataset of five chosen products provided by an industry partner. Results are compared to the baseline linear model and show improvement of over 21%.

Predictive control and optimization in buildings proved to be a promising approach in increasing energy efficiency of the sector as one of the largest energy consumer. Zone digitalization is still one of the ongoing issues in older buildings, and has only recently started to be interesting in the residential sector due to high prices of required expert knowledge and automation equipment. However, buildings systems digitalization and networking also brought to fore the system security issues. Distributed approach to zone digitalization and predictive control, enabled by recent advances in embedded technology, implies both hardware topology and control algorithm structure. The paper focuses on a case where each zone holds a separate controller with tailored temperature setpoint prediction and model predictive control algorithm, which independently calculate the optimal heating control laws of the corresponding zones. Furthermore, the controllers are mutually and iteratively bidding toward the joint energy efficiency goal of the whole building. Such control structure enables fast digitalization and optimal joint operation of the building while keeping the independency of the users and retaining the data privacy. Only essential data is transmitted to the central coordinator in form of a summed information, which cannot extrapolate particular user data. Additionally, single zone controller security breach does not inflict damage to the whole system. System resiliency to security issues is therefore strongly increased.