EmotIoT: an IoT system to improve users’ wellbeing

IoT provides applications and possibilities to improve people’s daily lives and business environments. However, most of these technologies have not been exploited in the field of emotions. With the amount of data that can be collected through IoT, emotions could be detected and anticipated. Since the study of related works indicates a lack of methodological approaches in designing IoT systems from the perspective of emotions and smart adaption rules, we introduce a methodology that can help design IoT systems quickly in this scenario, where the detection of users is valuable. In order to test the methodology presented, we apply the proposed stages to design an IoT smart recommender system named EmotIoT. The system allows anticipating and predicting future users’ emotions using parameters collected from IoT devices. It recommends new activities for the user in order to obtain a final state. Test results validate our recommender system as it has obtained more than 80% accuracy in predicting future user emotions

Aprendizaje móvil: perspectivas

El futur de l’aprenentatge, des d’una perspectiva tècnica, està integrat per quatre eixos que el defineixen i sobre els quals s’articulen esforços tecnològics i metodològics. Aquests eixos són: la mobilitat, la interacció, la intel·ligència artificial i recursos basats en tecnologia com la realitat augmentada i els jocs aplicats a l’aprenentatge. La seva combinació suposa la creació d’un model d’escenaris mòbils, interactius i intel·ligents que aprofiten tots els espais i temps disponibles per a l’aprenent. Les diferents tecnologies, cadascuna per la seva banda, ja estan disponibles i són utilitzades en diverses experiències educatives; el que cal és la conjugació d’aquestes experiències a través de models didàctics en els quals l’aprenentatge assolit pels estudiants sigui significatiu. En aquest article es discuteixen aquestes tecnologies i es planteja un model d’integració que possibilita l’establiment d’un marc referencial de treball didàctic. Es conclou la necessitat d’experimentar tecnologies i plasmar-ne els resultats en models d’ensenyament-aprenentatge que utilitzin esquemes d’interacció alternatius i la urgència de disposar de sistemes tutorials intel·ligents per a massificar la tutoria.From a technical perspective, the future of learning is defined by four axes around which technological and methodological efforts revolve. These axes are mobility, interaction, artificial intelligence and technology-based resources such as augmented reality and games applied to learning. Combining them means creating a model of mobile, interactive and intelligent scenarios that take advantage of the spaces and times available to the learner. The various technologies are already available yet used separately in different educational experiences. It is therefore crucial to combine and integrate them into didactic models wherein the learning attained by students is significant. This article discusses these technologies and proposes an integrative model that enables a framework of reference for didactic work to be established. It concludes by highlighting the need to experiment with technologies and to apply the results to teaching-learning models using alternative interaction schema, and the urgency of having intelligent tutoring systems to make tutoring available on a massive scale.El futuro del aprendizaje, desde una perspectiva técnica, está integrado por cuatro ejes que lo definen y sobre los que se articulan esfuerzos tecnológicos y metodológicos. Estos ejes son: la movilidad, la interacción, la inteligencia artificial y recursos basados en tecnología como la realidad aumentada y los juegos aplicados al aprendizaje. Su combinación supone la creación de un modelo de escenarios móviles, interactivos e inteligentes que aprovechan todos los espacios y tiempos disponibles para el aprendiente. Las distintas tecnologías, cada una por su lado, ya están disponibles y son utilizadas en diversas experiencias educativas; lo que se hace necesario es la conjugación de estas a través de modelos didácticos en los que el aprendizaje alcanzado por los estudiantes sea significativo. En este artículo se discuten estas tecnologías y se plantea un modelo de integración que posibilita el establecimiento de un marco referencial de trabajo didáctico. Se concluye la necesidad de experimentar tecnologías y plasmar los resultados en modelos de enseñanza-aprendizaje que utilicen esquemas de interacción alternativos y la urgencia de contar con sistemas tutoriales inteligentes para masificar la tutoría

Recommendation with User Trust and Item Rating

Recommender systems is becomes widespread and utilized in several fields for gathering the knowledge supported the user necessities. It�s in the main wont to facilitate the user for accessing the method supported the relevant data. Several framework for recommendation systems supported the various algorithms area unit revolve round the idea of accuracy solely however alternative necessary feature like diversity of the recommendations area unit neglected. The main idea of these works is that not only incorporating demographic information of users in profile matching process of CF-based algorithms is important weighting should be assigned to these features including rating feature the motivation behind this idea is that �different users place different importance or priority on each feature of the user � profile. For example if a male user prefers to be given recommendations based on the opinions of the other men then his feature weight for gender would be higher than other features�. Here we apply improved invasive weed optimization (IIWO) algorithm for the same purpose with some little changes in selecting the potential similar users as described in the previous sub section and in the evaluation criteria. After the optima weights have been found the two profiles are compared according to equation based on the Euclidean distance of the two profiles

Social Machinery and Intelligence

Social machines are systems formed by technical and human elements interacting in a structured manner. The use of digital platforms as mediators allows large numbers of human participants to join such mechanisms, creating systems where interconnected digital and human components operate as a single machine capable of highly sophisticated behaviour. Under certain conditions, such systems can be described as autonomous and goal-driven agents. Many examples of modern Artificial Intelligence (AI) can be regarded as instances of this class of mechanisms. We argue that this type of autonomous social machines has provided a new paradigm for the design of intelligent systems marking a new phase in the field of AI. The consequences of this observation range from methodological, philosophical to ethical. On the one side, it emphasises the role of Human-Computer Interaction in the design of intelligent systems, while on the other side it draws attention to both the risks for a human being and those for a society relying on mechanisms that are not necessarily controllable. The difficulty by companies in regulating the spread of misinformation, as well as those by authorities to protect task-workers managed by a software infrastructure, could be just some of the effects of this technological paradigm

Modeling item--item similarities for personalized recommendations on Yahoo! front page

We consider the problem of algorithmically recommending items to users on a Yahoo! front page module. Our approach is based on a novel multilevel hierarchical model that we refer to as a User Profile Model with Graphical Lasso (UPG). The UPG provides a personalized recommendation to users by simultaneously incorporating both user covariates and historical user interactions with items in a model based way. In fact, we build a per-item regression model based on a rich set of user covariates and estimate individual user affinity to items by introducing a latent random vector for each user. The vector random effects are assumed to be drawn from a prior with a precision matrix that measures residual partial associations among items. To ensure better estimates of a precision matrix in high-dimensions, the matrix elements are constrained through a Lasso penalty. Our model is fitted through a penalized-quasi likelihood procedure coupled with a scalable EM algorithm. We employ several computational strategies like multi-threading, conjugate gradients and heavily exploit problem structure to scale our computations in the E-step. For the M-step we take recourse to a scalable variant of the Graphical Lasso algorithm for covariance selection. Through extensive experiments on a new data set obtained from Yahoo! front page and a benchmark data set from a movie recommender application, we show that our UPG model significantly improves performance compared to several state-of-the-art methods in the literature, especially those based on a bilinear random effects model (BIRE). In particular, we show that the gains of UPG are significant compared to BIRE when the number of users is large and the number of items to select from is small. For large item sets and relatively small user sets the results of UPG and BIRE are comparable. The UPG leads to faster model building and produces outputs which are interpretable.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS475 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

Wide-Scale Automatic Analysis of 20 Years of ITS Research

The analysis of literature within a research domain can provide significant value during preliminary research. While literature reviews may provide an in-depth understanding of current studies within an area, they are limited by the number of studies which they take into account. Importantly, whilst publications in hot areas abound, it is not feasible for an individual or team to analyse a large volume of publications within a reasonable amount of time. Additionally, major publications which have gained a large number of citations are more likely to be included in a review, with recent or fringe publications receiving less inclusion. We provide thus an automatic methodology for the large-scale analysis of literature within the Intelligent Tutoring Systems (ITS) domain, with the aim of identifying trends and areas of research from a corpus of publications which is significantly larger than is typically presented in conventional literature reviews. We illustrate this by a novel analysis of 20 years of ITS research. The resulting analysis indicates a significant shift of the status quo of research in recent years with the advent of novel neural network architectures and the introduction of MOOCs

Convergence of Gamification and Machine Learning: A Systematic Literature Review

Recent developments in human–computer interaction technologies raised the attention towards gamification techniques, that can be defined as using game elements in a non-gaming context. Furthermore, advancement in machine learning (ML) methods and its potential to enhance other technologies, resulted in the inception of a new era where ML and gamification are combined. This new direction thrilled us to conduct a systematic literature review in order to investigate the current literature in the field, to explore the convergence of these two technologies, highlighting their influence on one another, and the reported benefits and challenges. The results of the study reflect the various usage of this confluence, mainly in, learning and educational activities, personalizing gamification to the users, behavioral change efforts, adapting the gamification context and optimizing the gamification tasks. Adding to that, data collection for machine learning by gamification technology and teaching machine learning with the help of gamification were identified. Finally, we point out their benefits and challenges towards streamlining future research endeavors.publishedVersio