Numerous advances in the area of ubiquitous computing resulted in appearance of such paradigms as Smart Spaces, where a number of devices can use a shared view of resources and services. Nowadays we observing emerge of the smart space technologies, which are truly mobile and allowing equally efficient design of applications for the broad range of devices. An example of such smart spaces platform is Smart-M3, which has been developed by Nokia in cooperation with the SOFIA program partners. The paper presents an approach for applying Smart-M3 smart space solution for multimedia service networks. This enables a convenient and seamless access to the distributed multimedia content from the mobile devices, PCs and other types of user devices. A scenario of multimedia content sharing between various companies and their customers is presented as one of the possible applications of the proposed solution and the corresponding demo implementation is developed.

Cyber-Physical-Social Systems (CPSSs) integrate various resources from physical, cyber, and social worlds. Efficient interaction of these resources is essential for CPSSs operation. Ontologies do not only provide for semantic operability between different resources but also provide means to create sharable ontology-based context models specified for actual settings. Usage of the context supports situation-driven behavior of CPSSs resources and thus is an enabler for their self-organisation. The present research inherits the idea of context ontologies usage for modelling context in CPSSs. In this work, an upper level context ontology for CPSSs is proposed. This ontology is applied in the domain of self-organising resource network.

The paper presents category classification of mobile travel applications accessible at the moment for tourists in application stores for most popular mobile operation systems (Android and iOS). The most interesting category is "Travel Guides" that combines "Information Resources" and "Location-Based Services" category. Authors propose application "Tourist assistant - TAIS" that is related to "Travel Guides" category and recommends the tourist attractions around. Information about attractions is extracted from different internet sources.

One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something wrong or suspicious with his/her health condition. The most popular and accurate ways to measure blood pressure are cuff-based, inconvenient, and pricey, but on the bright side, many experimental studies prove that changes in the color intensities of the RGB channels represent variation in the blood that flows beneath the skin, which is strongly related to blood pressure; hence, we present a novel approach to blood pressure estimation based on the analysis of human face video using hybrid deep learning models. We deeply analyzed proposed approaches and methods to develop combinations of state-of-the-art models that were validated by their testing results on the Vision for Vitals (V4V) dataset compared to the performance of other available proposed models. Additionally, we came up with a new metric to evaluate the performance of our models using Pearson’s correlation coefficient between the predicted blood pressure of the subjects and their respiratory rate at each minute, which is provided by our own dataset that includes 60 videos of operators working on personal computers for almost 20 min in each video. Our method provides a cuff-less, fast, and comfortable way to estimate blood pressure with no need for any equipment except the camera of your smartphone.

The advantage of human-machine collective intelligence for decision support systems is the ability to make better decisions due to the mitigation of human biases in the generation of potential solutions and their evaluation. So far, the potential of human-machine collective intelligence was used only in few decision support systems, however, teamwork between humans and machines has not been achieved. This is partly due to the lack of interoperability in these systems. In earlier works, the authors proposed the apparatus of multi-aspect ontologies implying the integration of multiple domain ontologies to provide interoperability between humans and machines and coordinate interrelated processes going on in the systems of the considered type. Such ontologies have proved efficient for systems that require intensive information and knowledge exchange between loosely-related dynamic autonomous domains (e.g., enterprise knowledge management, product lifecycle management, or human-machine collective intelligence systems). However, existing ontology development methodologies fail to recommend a process that would support cross-domain knowledge integration during the multi-aspect ontology development. Moreover, the structure of the multi-aspect ontology imposes some restrictions on the integration approach. The paper proposes such a methodology for the multi-aspect ontology development that incorporates the aspect integration approach at multiple levels. The methodology is applied to develop a multi-aspect ontology for decision support based on human-machine collective intelligence. An example from the “e-tourism” domain demonstrates the applicability of the proposed methodology as well as the usage of the multi-aspect ontology for a human-machine environment aimed at solving real-world problems. The proposed methodology can facilitate the development of ontologies for complex knowledge-based systems that operate with knowledge from multiple loosely-connected domains.