Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and $K$-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

Care burden, loneliness, and social isolation in caregivers of people with physical and brain health conditions in English-speaking regions: Before and during the COVID-19 pandemic.

BACKGROUND: Public health restrictions due to the COVID-19 (SARS CoV-2) pandemic have disproportionately affected informal caregivers of people living with long term health conditions. We aimed to explore levels of care burden, loneliness, and social isolation among caregivers of people with enduring physical and brain health conditions in English-speaking regions worldwide, by investigating outcomes before and during the COVID-19 pandemic. METHODS: A cross-sectional anonymous online survey data from 2287 English-speaking caregivers of people with long term health conditions from four English-speaking regions (UK, Ireland, USA, New Zealand) included measures of care burden, loneliness, and social isolation, reported before and during the COVID-19 pandemic. Analyses were descriptive, followed by an ordinal regression model for predictors of burden. RESULTS: Compared to pre-pandemic levels, all caregivers experienced a significant increase in burden, loneliness, and isolation. Caregivers of people with both brain health and physical conditions were the most burdened and had the highest levels of loneliness and isolation compared to caregivers of people with either a brain health or physical condition only. The increase in care burden among caregivers of people with brain health challenges was associated with caregiver's gender, moderate and severe emotional loneliness, magnitude and frequency of isolation during the pandemic, and care circumstances (cohabitation with the care recipient, restrictions on the ability to provide care). CONCLUSIONS: Health and social care interventions should target caregivers' care circumstances and psychological outcomes, particularly in women, accounting for the significant additional burden of care, loneliness, and isolation resulting from pandemic-related restrictions

New image descriptor from edge detector and blob extractor

In this paper we present a novel approach for image description. The method is based on two well-known algorithms: edge detection and blob extraction. In the edge detection step we use the Canny detector. Our method provides a mathematical description of each object in the input image. On the output of the presented algorithm we obtain a histogram, which can be used in various fields of computer vision. In this paper we applied it in the content-based image retrieval system. The simulations proved the effectiveness of our method