8,976 research outputs found
Ubiquitous learning architecture to enable learning path design across the cumulative learning continuum
The past twelve years have seen ubiquitous learning (u-learning) emerging as a new learning paradigm based on ubiquitous technology. By integrating a high level of mobility into the learning environment, u-learning enables learning not only through formal but also through informal and social learning modalities. This makes it suitable for lifelong learners that want to explore, identify and seize such learning opportunities, and to fully build upon these experiences. This paper presents a theoretical framework for designing personalized learning paths for lifelong learners, which supports contemporary pedagogical approaches that can promote the idea of a cumulative learning continuum from pedagogy through andragogy to heutagogy where lifelong learners progress in maturity and autonomy. The framework design builds on existing conceptual and process models for pedagogy-driven design of learning ecosystems. Based on this framework, we propose a system architecture that aims to provide personalized learning pathways using selected pedagogical strategies, and to integrate formal, informal and social training offerings using two well-known learning and development reference models; the 70:20:10 framework and the 3ā33 model
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
User model interoperability in education: sharing learner data using the experience API and distributed ledger technology
Learning analytics and data mining require gathering and exchanging learner data for further processing and designing of activities tailored to learnerās characteristics, context, and needs. Currently, systems that store learnersā attributes should, ideally, be operated and controlled by responsible and trustworthy authorities that guarantee the protection and sovereignty of data and use objective criteria to protect and represent all partiesā interests. This chapter introduces a peer-to-peer method for storing and exchanging learner data with minimal trust. The proposed approach, underpinned by the Experience API standard, eliminates the need of a mediator authority by using distributed ledger technology
Trends in Cloud Computing Paradigms: Fundamental Issues, Recent Advances, and Research Directions toward 6G Fog Networks
There has been significant research interest in various computing-based paradigms such as cloud computing, Internet of Things, fog computing, and edge computing, due to their various associated advantages. In this chapter, we present a comprehensive review of these architectures and their associated concepts. Moreover, we consider different enable technologies that facilitate computing paradigm evolution. In this context, we focus mainly on fog computing considering its related fundamental issues and recent advances. Besides, we present further research directions toward the sixth generation fog computing paradigm
AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems
The evolution towards 6G architecture promises a transformative shift in
communication networks, with artificial intelligence (AI) playing a pivotal
role. This paper delves deep into the seamless integration of Large Language
Models (LLMs) and Generalized Pretrained Transformers (GPT) within 6G systems.
Their ability to grasp intent, strategize, and execute intricate commands will
be pivotal in redefining network functionalities and interactions. Central to
this is the AI Interconnect framework, intricately woven to facilitate
AI-centric operations within the network. Building on the continuously evolving
current state-of-the-art, we present a new architectural perspective for the
upcoming generation of mobile networks. Here, LLMs and GPTs will
collaboratively take center stage alongside traditional pre-generative AI and
machine learning (ML) algorithms. This union promises a novel confluence of the
old and new, melding tried-and-tested methods with transformative AI
technologies. Along with providing a conceptual overview of this evolution, we
delve into the nuances of practical applications arising from such an
integration. Through this paper, we envisage a symbiotic integration where AI
becomes the cornerstone of the next-generation communication paradigm, offering
insights into the structural and functional facets of an AI-native 6G network
IntegraĆ§Ć£o de localizaĆ§Ć£o baseada em movimento na aplicaĆ§Ć£o mĆ³vel EduPARK
More and more, mobile applications require precise localization solutions in a variety of environments. Although GPS is widely used as localization solution, it may present some accuracy problems in special conditions such as unfavorable weather or spaces with multiple obstructions such as public parks. For these scenarios, alternative solutions to GPS are of extreme relevance and are widely studied recently. This dissertation studies the case of EduPARK application, which is an augmented reality application that is implemented in the Infante D. Pedro park in Aveiro. Due to the poor accuracy of GPS in this park, the implementation of positioning and marker-less augmented reality functionalities presents difficulties. Existing relevant systems are analyzed, and an architecture based on pedestrian dead reckoning is proposed. The corresponding implementation is presented, which consists of a positioning solution using the sensors available in the smartphones, a step detection algorithm, a distance traveled estimator, an orientation estimator and a position estimator. For the validation of this solution, functionalities were implemented in the EduPARK application for testing purposes and usability tests performed. The results obtained show that the proposed solution can be an alternative to provide accurate positioning within the Infante D. Pedro park, thus enabling the implementation of functionalities of geocaching and marker-less augmented reality.Cada vez mais, as aplicaƧƵes mĆ³veis requerem soluƧƵes de localizaĆ§Ć£o precisa nos mais variados ambientes. Apesar de o GPS ser amplamente usado como soluĆ§Ć£o para localizaĆ§Ć£o, pode apresentar alguns problemas de precisĆ£o em condiƧƵes especiais, como mau tempo, ou espaƧos com vĆ”rias obstruƧƵes, como parques pĆŗblicos. Para estes casos, soluƧƵes alternativas ao GPS sĆ£o de extrema relevĆ¢ncia e veem sendo desenvolvidas. A presente dissertaĆ§Ć£o estuda o caso do projeto EduPARK, que Ć© uma aplicaĆ§Ć£o mĆ³vel de realidade aumentada para o parque Infante D. Pedro em Aveiro. Devido Ć fraca precisĆ£o do GPS nesse parque, a implementaĆ§Ć£o de funcionalidades baseadas no posionamento e de realidade aumentada sem marcadores apresenta dificuldades. SĆ£o analisados sistemas relevantes existentes e Ć© proposta uma arquitetura baseada em localizaĆ§Ć£o de pedestres. Em seguida Ć© apresentada a correspondente implementaĆ§Ć£o, que consiste numa soluĆ§Ć£o de posicionamento usando os sensores disponiveis nos smartphones, um algoritmo de deteĆ§Ć£o de passos, um estimador de distĆ¢ncia percorrida, um estimador de orientaĆ§Ć£o e um estimador de posicionamento. Para a validaĆ§Ć£o desta soluĆ§Ć£o, foram implementadas funcionalidades na aplicaĆ§Ć£o EduPARK para fins de teste, e realizados testes com utilizadores e testes de usabilidade. Os resultados obtidos demostram que a soluĆ§Ć£o proposta pode ser uma alternativa para a localizaĆ§Ć£o no interior do parque Infante D. Pedro, viabilizando desta forma a implementaĆ§Ć£o de funcionalidades baseadas no posicionamento e de realidade aumenta sem marcadores.EduPARK Ć© um projeto financiado por Fundos FEDER atravĆ©s do Programa Operacional Competitividade e InternacionalizaĆ§Ć£o - COMPETE 2020 e por Fundos Nacionais atravĆ©s da FCT - FundaĆ§Ć£o para a CiĆŖncia e a Tecnologia no Ć¢mbito do projeto POCI-01-0145-FEDER-016542.Mestrado em Engenharia InformĆ”tic
Gamification of Authoring Interactive E-Books for Children: The Q-Tales Ecosystem
The e-book industry is reshaping the norm of traditional book publishing and most publishing houses are concentrating their efforts in digital, in order to satisfy new market needs and capture significant market share. Currently, one of out of five e-books sold, are children-related and overall, the e-book industry is projected to be valued at $18.9 billion by 2018. Nevertheless, the increased market penetration of independent writers accompanied with continuous technological improvements leads to new challenges for the stakeholders involved, as a growing number of individuals with limited resources attempt to compete against traditional publishing houses. The Q-Tales ecosystem aims to support the community of creative professionals, experts and parents co-create new (or transform existing) children literature into high quality interactive e-books.
At this new disruptive approach of self-publishing, the gamification paradigm was employed, creating game-like experiences, to motivate professionals participate in the process and adopt it. The present study focuses on the gamification aspect of Q-Tales as means to drive engagement with the entire ecosystem and promote its appropriate use, enhancing the overall goal of creating interactive children e- books. The gamification design of the Q-Tales distributed system for collaborative authoring of interactive e-books for children is presented and discussed as a case study of gamification of electronic services. More specifically, game elements, such as points, leaderboards, badges, missions and feedback were infused in the architectural units of the platform, in correspondence to the overall development of the Q-Tales Gamification Framework
Collaborative autonomy in heterogeneous multi-robot systems
As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition.
This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems.
Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots
Efficient Data Streaming Analytic Designs for Parallel and Distributed Processing
Today, ubiquitously sensing technologies enable inter-connection of physical\ua0objects, as part of Internet of Things (IoT), and provide massive amounts of\ua0data streams. In such scenarios, the demand for timely analysis has resulted in\ua0a shift of data processing paradigms towards continuous, parallel, and multitier\ua0computing. However, these paradigms are followed by several challenges\ua0especially regarding analysis speed, precision, costs, and deterministic execution.\ua0This thesis studies a number of such challenges to enable efficient continuous\ua0processing of streams of data in a decentralized and timely manner.In the first part of the thesis, we investigate techniques aiming at speeding\ua0up the processing without a loss in precision. The focus is on continuous\ua0machine learning/data mining types of problems, appearing commonly in IoT\ua0applications, and in particular continuous clustering and monitoring, for which\ua0we present novel algorithms; (i) Lisco, a sequential algorithm to cluster data\ua0points collected by LiDAR (a distance sensor that creates a 3D mapping of the\ua0environment), (ii) p-Lisco, the parallel version of Lisco to enhance pipeline- and\ua0data-parallelism of the latter, (iii) pi-Lisco, the parallel and incremental version\ua0to reuse the information and prevent redundant computations, (iv) g-Lisco, a\ua0generalized version of Lisco to cluster any data with spatio-temporal locality\ua0by leveraging the implicit ordering of the data, and (v) Amble, a continuous\ua0monitoring solution in an industrial process.In the second part, we investigate techniques to reduce the analysis costs\ua0in addition to speeding up the processing while also supporting deterministic\ua0execution. The focus is on problems associated with availability and utilization\ua0of computing resources, namely reducing the volumes of data, involving\ua0concurrent computing elements, and adjusting the level of concurrency. For\ua0that, we propose three frameworks; (i) DRIVEN, a framework to continuously\ua0compress the data and enable efficient transmission of the compact data in the\ua0processing pipeline, (ii) STRATUM, a framework to continuously pre-process\ua0the data before transferring the later to upper tiers for further processing, and\ua0(iii) STRETCH, a framework to enable instantaneous elastic reconfigurations\ua0to adjust intra-node resources at runtime while ensuring determinism.The algorithms and frameworks presented in this thesis contribute to an\ua0efficient processing of data streams in an online manner while utilizing available\ua0resources. Using extensive evaluations, we show the efficiency and achievements\ua0of the proposed techniques for IoT representative applications that involve a\ua0wide spectrum of platforms, and illustrate that the performance of our work\ua0exceeds that of state-of-the-art techniques
- ā¦