Ubiquitous Computing is moving the interaction away from the human-computer paradigm and towards the creation of smart environments that users and things, from the IoT perspective, interact with. User modeling and adaptation is consistently present having the human user as a constant but pervasive interaction introduces the need for context incorporation towards context-aware smart environments. The current article discusses both aspects of the user modeling and adaptation as well as context awareness and incorporation into the smart home domain. Users are modeled as fuzzy personas and these models are semantically related. Context information is collected via sensors and corresponds to various aspects of the pervasive interaction such as temperature and humidity, but also smart city sensors and services. This context information enhances the smart home environment via the incorporation of user defined home rules. Semantic Web technologies support the knowledge representation of this ecosystem while the overall architecture has been experimentally verified using input from the SmartSantander smart city and applying it to the SandS smart home within FIRE and FIWARE frameworks.

The ability to learn robust, resizable feature representations from unlabeled data has potential applications in a wide variety of machine learning tasks. One way to create such representations is to train deep generative models that can learn to capture the complex distribution of real-world data. Generative adversarial network (GAN) approaches have shown impressive results in producing generative models of images, but relatively little work has been done on evaluating the performance of these methods for the learning representation of natural language, both in supervised and unsupervised settings at the document, sentence, and aspect level. Extensive research validation experiments were performed by leveraging the 20 Newsgroups corpus, the Movie Review (MR) Dataset, and the Finegrained Sentiment Dataset (FSD). Our experimental analysis suggests that GANs can successfully learn representations of natural language texts at all three aforementioned levels.

The scientific study of teamwork in the context of long-term spaceflight has uncovered a considerable amount of knowledge over the past 20 years. Although much is known about the underlying factors and processes of teamwork, much is left to be discovered for teams who operate in extreme isolation conditions during spaceflights. Thus, special considerations must be made to enhance teamwork and team well-being for long-term missions during which the team will live and work together. Being affected by both mental and physical stress during interactional context conversations might have a direct or indirect impact on team members’ speech acoustics, facial expressions, lexical choices and their physiological responses. The purpose of this article is (a) to illustrate the relationship between the modalities of vocal-acoustic, language and physiological cues during stressful teammate conversations, (b) to delineate promising research paths to help further our insights into understanding the underlying mechanisms of high team cohesion during spaceflights, (c) to build upon our preliminary experimental results that were recently published, using a dyadic team corpus during the demanding operational task of “diffusing a bomb” and (d) to outline a list of parameters that should be considered and examined that would be useful in spaceflights for team-effectiveness research in similarly stressful conditions. Under this view, it is expected to take us one step towards building an extremely non-intrusive and relatively inexpensive set of measures deployed in ground analogs to assess complex and dynamic behavior of individuals.