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It is well known that learners using intelligent learning environments make different use of the feedback provided by the intelligent learning environment and exhibit different patterns of behaviour. Traditional approaches to measuring such behaviour have focused on observational methods, think-aloud protocols, ratings and log data. More recently, the field of educational neuroscience has placed greater emphasis on real-time measures using eye tracking, electroencephalogram and functional magnetic resonance imaging. Our work fits into the latter vein. Drawing on a literature review from cognitive psychology, neuroscience and education, we describe our Learner Processing of Feedback in Intelligent Learning Environments model of how learners process feedback. We also present findings from a pilot study as a preliminary test of the model. Seventeen learners participated in an experiment using the intelligent learning environment known as Crystal Island. A range of data was collected, including a pre-test measuring prior knowledge, think-alouds, log data, video recordings, biometrics and post-task questionnaires. We discuss these findings and steps forward to further validate the model using physiological measures. [Author abstract]

The emergence of the Ambient Intelligence (AmI) paradigm and the proliferation of Internet of Things (IoT) devices and services unveiled new potentials for the domain of domestic living, where the line between “the computer” and the (intelligent) environment becomes altogether invisible. Particularly, the residents of a house can use the living room not only as a traditional social and individual space where many activities take place, but also as a smart ecosystem that (a) enhances leisure activities by providing a rich suite of entertainment applications, (b) implements a home control middleware, (c) acts as an intervention host that is able to display appropriate content when the users need help or support, (d) behaves as an intelligent agent that communicates with the users in a natural manner and assists them throughout their daily activities, (e) presents a notification hub that provides personalized alerts according to contextual information, and (f) becomes an intermediary communication center for the family. This paper (i) describes how the “Intelligent Living Room” realizes these newly emerged roles, (ii) presents the process that was followed in order to design the living room environment, (iii) introduces the hardware and software facilities that were developed in order to improve quality of life, and (iv) reports the findings of various evaluation experiments conducted to assess the overall User Experience (UX).

Purpose The Reflective Middleware for Acoustic Management (ReM-AM), based on the Middleware for Cloud Learning Environments (AmICL), aims to improve the interaction between users and agents in a Smart Environment (SE) using acoustic services, in order to consider the unpredictable situations due to the sounds and vibrations. The middleware allows observing, analyzing, modifying and interacting in every state of a SE from the acoustics. This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management.Design/methodology/approach This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. In this paper are defined the different domains of knowledge required for the management of the sounds in SEs, which are modeled using ontologies.Findings This work proposes an acoustics and sound ontology, a service-oriented architecture (SOA) ontology, and a data analytics and autonomic computing ontology, which work together. Finally, the paper presents three case studies in the context of smart workplace (SWP), ambient-assisted living (AAL) and Smart Cities (SC).Research limitations/implications Future works will be based on the development of algorithms for classification and analysis of sound events, to help with emotion recognition not only from speech but also from random and separate sound events. Also, other works will be about the definition of the implementation requirements, and the definition of the real context modeling requirements to develop a real prototype.Practical implications In the case studies is possible to observe the flexibility that the ReM-AM middleware based on the ODA paradigm has by being aware of different contexts and acquire information of each, using this information to adapt itself to the environment and improve it using the autonomic cycles. To achieve this, the middleware integrates the classes and relations in its ontologies naturally in the autonomic cycles.Originality/value The main contribution of this work is the description of the ontologies required for future works about acoustic management in SE, considering that what has been studied by other works is the utilization of ontologies for sound event recognition but not have been expanded like knowledge source in an SE middleware. Specifically, this paper presents the theoretical framework of this work composed of the AmICL middleware, ReM-AM middleware and the ODA paradigm.

A personalized framework for smart home automation is introduced, utilizing machine learning to predict user activities and allow for the context-aware control of living spaces. Predicting user activities, such as ‘Watch_TV’, ‘Sleep’, ‘Work_On_Computer’, and ‘Cook_Dinner’, is essential for improving occupant comfort, optimizing energy consumption, and offering proactive support in smart home settings. The Edge Light Human Activity Recognition Predictor, or EL-HARP, is the main prediction model used in this framework to predict user behavior. The system combines open-source software for real-time sensing, facial recognition, and appliance control with affordable hardware, including the Raspberry Pi 5, ESP32-CAM, Tuya smart switches, NFC (Near Field Communication), and ultrasonic sensors. In order to predict daily user activities, three gradient-boosting models—XGBoost, CatBoost, and LightGBM (Gradient Boosting Models)—are trained for each household using engineered features and past behaviour patterns. Using extended temporal features, LightGBM in particular achieves strong predictive performance within EL-HARP. The framework is optimized for edge deployment with efficient training, regularization, and class imbalance handling. A fully functional prototype demonstrates real-time performance and adaptability to individual behavior patterns. This work contributes a scalable, privacy-preserving, and user-centric approach to intelligent home automation.

This viewpoint article proposes an enabling framework that identifies the use of various blockchain technologies in tourism and their applications (digitalization, automation, disintermediation, and intelligent environment) across the different stages of travel (pre-trip, during the trip, and post-trip). As we know, the tourism sector contributes immensely to world GDP and job creation. However, the COVID-19 pandemic, even after two years since it first appeared, continues to adversely impact the tourism prospects of countries across the world due to nationwide lockdowns and travel restrictions. As the world tries to adapt to the “new normal,“ the tourism sector is forced to re-think its ways of doing business and bring about innovations to facilitate the new norms of contactless and safe transactions. Also, the sector, more than ever, need to effectively deal with its inherent challenges such as transparency and credibility of information, fraudulent practices, opportunistic behavior of intermediaries, and foreign currency risks. Blockchain technology can transform the tourism sector by offering innovative solutions that address its pressing issues. However, our current understanding of blockchain application in tourism is quite limited, with previous work being largely fragmented and narrow in terms of both scope and application. We foresee that the insights offered in this viewpoint, including the framework, will advance both theory and practice and facilitate the implementation of blockchain-enabled solutions across different travel stages.

As the average age of the urban population increases, cities must adapt to improve the quality of life of their citizens. The City4Age H2020 project is working on the early detection of the risks related to mild cognitive impairment and frailty and on providing meaningful interventions that prevent these risks. As part of the risk detection process, we have developed a multilevel conceptual model that describes the user behaviour using actions, activities, and intra- and inter-activity behaviour. Using this conceptual model, we have created a deep learning architecture based on long short-term memory networks (LSTMs) that models the inter-activity behaviour. The presented architecture offers a probabilistic model that allows us to predict the user’s next actions and to identify anomalous user behaviours.

Emerging intelligent environments are considered to offer significant opportunities to positively impact human life, both at an individual and at a societal level, and in particular to provide useful means to support people in their daily life activities and thus improve well-being for everybody, especially for older people and for people with limitations of activities. In this context, accessibility and usability, although necessary, are not sufficient to ensure that applications and services are appropriately designed to satisfy human needs and overcome potential functional limitations in the execution of everyday activities fundamental for well-being. This position paper puts forward the claim that, in order to achieve the above objective, it is necessary that: (i) the design of Assistive Intelligent Environments is centered around the well-being of people, roughly intended as the possibility of executing the (everyday) human activities necessary for living (independently), thus emphasizing usefulness in addition to usability; (ii) the technological environment is orchestrated around such activities and contains knowledge about how they are performed and how people need to be supported to perform them; (iii) the environment makes use of monitoring and reasoning capabilities in order to adapt, fine-tune and evolve over time the type and level of support provided, and this process takes place considering ethical values; (iv) the applications must also support the possibility of contact with other people, who in many cases may be the only effective help. Moving forward from the Design for All paradigm, this paper discusses how the latter can be revisited under the perspective of technology’s usefulness and contribution to human well-being. Subsequently, it introduces a practical notion of well-being based on the ICF classification of human functions and activities and discusses how such notion can constitute the starting point and the focus of design approaches targeted to assist people in their everyday life mainly (but not exclusively) in the home environment. As a subsequent step, the need for integrating Artificial Intelligence capabilities in assistive intelligent environments is discussed, based on the complexity of the human problems to be addressed and the diversity of the types of support needed. The proposed approach is exemplified and illustrated through the experience acquired in the development of four applications, addressing vital aspects of human life, namely nutrition, stress management, sleep management and counteracting loneliness. Finally, based on the acquired experience, the need to take into account ethical values in the development of assistive intelligent environments is discussed.

Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.

‘User Experience’ (UX) is a term that has been established in HCI research and practice, subsuming the term ‘usability’. UX denotes that interaction with a contemporary technological system goes far beyond usability, extending to one’s emotions before, during, and after using the system and cannot be defined only by studying the fundamental usability attributes of effectiveness, efficiency and user satisfaction. Measuring UX becomes a substantially more complicated endeavor when the interaction target is not just a technological system or application, but an entire intelligent environment and the systems contained therein. Motivated by the imminent need to assess, measure and quantify user experience in intelligent environments, this paper presents a methodological and conceptual framework that provides concrete guidance for UX research, design and evaluation, explaining which UX parameter should be measured, how, and when. An evaluation of the framework indicated that it can be valuable for researchers and practitioners, assisting them in planning, carrying out, and analyzing UX studies in a comprehensive and thorough manner, thus enhancing their understanding and improving the experiences they design for intelligent environments.