Challenges in context-aware mobile language learning: the MASELTOV approach

Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment

Spatio-temporal patterns of human mobility from geo-social networks for urban computing: Analysis, models & applications

The availability of rich information about fine-grained user mobility in urban environments from increasingly geographically-aware social networking services and the rapid development of machine learning applications greatly facilitate the investigation of urban issues. In this setting, urban computing emerges intending to tackle a variety of challenges faced by cities nowadays and to offer promising approaches to improving our living environment. Leveraging massive amounts of data from geo-social networks with unprecedented richness, we show how to devise novel algorithmic techniques to reveal underlying urban mobility patterns for better policy-making and more efficient mobile applications in this dissertation. Building upon the foundation of existing research efforts in urban computing field and basic machine learning techniques, in this dissertation, we propose a general framework of urban computing with geo-social network data and develop novel algorithms tailored for three urban computing tasks. We begin by exploring how the transition data recording human movements between urban venues from geo-social networks can be aggregated and utilised to detect spatio-temporal changes of local graphs in urban areas. We further explore how this can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing areas by supervised machine learning methods. We then study how to extract latent patterns from collective user-venue interactions with the help of a spatio-temporal aware topic modeling approach for the benefit of urban infrastructure planning. After that, we propose a model to detect the gap between user-side demand and venue-side supply levels for certain types of services in urban environments to suggest further policymaking and investment optimisation. Finally, we address a mobility prediction task, the application aim of which is to recommend new places to explore in the city for mobile users. To this end, we develop a deep learning framework that integrates memory network and topic modeling techniques. Extensive experiments indicate that the proposed architecture can enhance the prediction performance in various recommendation scenarios with high interpretability. All in all, the insights drawn and the techniques developed in this dissertation make a substantial step in addressing issues in cities and open the door to future possibilities in the promising urban computing area

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

A multiple criteria route recommendation system

The work to be developed in this dissertation is part of a larger project called Sustainable Tourism Crowding (STC), which motivation is based on two negative impacts caused by the tourism overload that happens, particularly, in the historic neighborhoods of Lisbon. The goal of this dissertation is then to mitigate those problems: reduce the tourist burden of points of interest in a city that, in addition to the degradation of the tourist experience, causes sustainability problems in different aspects (environmental, social and local). Within the scope of this dissertation, the implementation of one component of a recommendation system is the proposed solution. It is based on a multi-criteria algorithm for recommending pedestrian routes that minimize the passage through more crowded places and maximizes the visit to sustainable points of interest. These routes will be personalized for each user, as they consider their explicit preferences (e.g. time, budget, physical effort) and several constraints taken from other microservices that are part of the global system architecture mentioned above (e.g. weather conditions, crowding levels, points of interest, sustainability). We conclude it is possible to develop a microservice that recommend personalized routes and communicate with other microservices that are part of the global system architecture mentioned above. The analysis of the experimental data from the recommendation system, allows us to conclude that it is possible to obtain a more balanced distribution of the tourist visit, by increasing the visit to more sustainable places of interest and avoiding crowded paths.O trabalho a desenvolver nesta dissertação insere-se num projeto de maior dimensão denominado Sustainable Tourism Crowding (STC), cuja motivação assenta, essencialmente, em dois impactos negativos provocados pela sobrecarga turística que se verifica, nomeadamente, nos bairros históricos de Lisboa. O objetivo desta dissertação é, então, mitigar esses problemas: reduzir a sobrecarga turística dos pontos de interesse mais visitados numa cidade que, além da degradação da experiência turística, causa problemas de sustentabilidade em diversos aspetos (ambiental, social e local). No âmbito desta dissertação, a implementação de um componente de um sistema de recomendação é a solução proposta. Baseia-se num algoritmo multicritério de recomendação de percursos pedonais que minimiza a passagem por locais mais apinhados e maximizam a visita a pontos de interesse mais sustentáveis. Essas rotas serão personalizadas para cada utilizador, pois consideram as suas preferências (por exemplo, tempo, orçamento, nível de esforço físico) e várias restrições retiradas de outros microsserviços que fazem parte da arquitetura do sistema global mencionado acima (por exemplo, condições meteorológicas, níveis de apinhamento, pontos de interesse, níveis de sustentabilidade). Concluímos que é possível desenvolver um microsserviço que recomenda rotas personalizadas e que comunica com outros microsserviços que fazem parte da arquitetura global do sistema mencionada acima. A análise dos dados experimentais do sistema de recomendação, permite-nos concluir que é possível obter uma distribuição mais equilibrada da visita turística, aumentando a visita a pontos de interesse mais sustentáveis e evitando percursos mais apinhados

Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth