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.

Recent advances in Affective Computing (AC) include research towards automatic analysis of human emotionally enhanced behavior during multiparty interactions within different contextual settings. Current paper delves on how is context incorporated into multiparty and multimodal interaction within the AC framework. Aspects of context incorporation such as importance and motivation for context incorporation, appropriate emotional models, resources of multiparty interactions useful for context analysis, context as another modality in multimodal AC and context-aware AC systems are addressed as research questions reviewing the current state-of-the-art in the research field. Challenges that arise from the incorporation of context are identified and discussed in order to foresee future research directions in the domain. Finally, we propose a context incorporation architecture into affect-aware systems with multiparty interaction including detection and extraction of semantic context concepts, enhancing emotional models with context information and context concept representation in appraisal estimation.

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