Pervasive Personal Information Spaces

Each user’s electronic information-interaction uniquely matches their information behaviour, activities and work context. In the ubiquitous computing environment, this information-interaction and the underlying personal information is distributed across multiple personal devices. This thesis investigates the idea of Pervasive Personal Information Spaces for improving ubiquitous personal information-interaction. Pervasive Personal Information Spaces integrate information distributed across multiple personal devices to support anytime-anywhere access to an individual’s information. This information is then visualised through context-based, flexible views that are personalised through user activities, diverse annotations and spontaneous information associations. The Spaces model embodies the characteristics of Pervasive Personal Information Spaces, which emphasise integration of the user’s information space, automation and communication, and flexible views. The model forms the basis for InfoMesh, an example implementation developed for desktops, laptops and PDAs. The design of the system was supported by a tool developed during the research called activity snaps that captures realistic user activity information for aiding the design and evaluation of interactive systems. User evaluation of InfoMesh elicited a positive response from participants for the ideas underlying Pervasive Personal Information Spaces, especially for carrying out work naturally and visualising, interpreting and retrieving information according to personalised contexts, associations and annotations. The user studies supported the research hypothesis, revealing that context-based flexible views may indeed provide better contextual, ubiquitous access and visualisation of information than current-day systems

Interpretable statistics for complex modelling: quantile and topological learning

As the complexity of our data increased exponentially in the last decades, so has our need for interpretable features. This thesis revolves around two paradigms to approach this quest for insights. In the first part we focus on parametric models, where the problem of interpretability can be seen as a “parametrization selection”. We introduce a quantile-centric parametrization and we show the advantages of our proposal in the context of regression, where it allows to bridge the gap between classical generalized linear (mixed) models and increasingly popular quantile methods. The second part of the thesis, concerned with topological learning, tackles the problem from a non-parametric perspective. As topology can be thought of as a way of characterizing data in terms of their connectivity structure, it allows to represent complex and possibly high dimensional through few features, such as the number of connected components, loops and voids. We illustrate how the emerging branch of statistics devoted to recovering topological structures in the data, Topological Data Analysis, can be exploited both for exploratory and inferential purposes with a special emphasis on kernels that preserve the topological information in the data. Finally, we show with an application how these two approaches can borrow strength from one another in the identification and description of brain activity through fMRI data from the ABIDE project

Wireless Telecommunications Issues: Cell Phone TV, Wireless Networks in Disaster Management, Ubiquitous Computing, and Adoption of Future Wireless Applications

This paper is a summary of a 2007 Association for Information Systems Americas Conference on Information Systems (AMCIS) panel discussion regarding current mobile wireless issues and technologies. The invited panelists are four faculty members specializing in information systems from the United States. The covered topics included cell phone TV and misconceptions surrounding it, wireless networks in disaster management, ubiquitous computing including anatomy of a mote and sensors, and the adoption of future wireless applications. First, we present wireless cell phone TV as a functioning multipurpose computer, or a Swiss army knife, of media devices. The misconceptions are stated, influenced by preconceived notions by the media critics as well as users. Next we discuss a range of wireless technologies including wearable computing, ad hoc and mesh wireless networks as a means of providing communications for first respondents during a natural or man-made disaster. Then we examine the anatomy of motes and RFIDs, including sensors, in an era of ubiquitous computing and a world of (inter-)connected objects. Finally, we discuss the socio-cultural constructs impacting users\u27 intentions to adopt future wireless applications

Security and Autonomic Management in System of Systems

A system of systems integrates systems that function independently but are networked together for a period of time to achieve a higher goal. These systems evolve over time and have emergent properties. Therefore, even with security controls in place, it is difficult to maintain a required level of security for the system of systems as a whole because uncertainties may arise at runtime. Uncertainties can occur from internal factors, such as malfunctions of a system, or from external factors, such as malicious attacks. Self-adaptation is an approach that allows a system to adapt in the face of such uncertainties without human intervention. This work outlines the progress made towards security mitigation in system of systems using a generic autonomic management system to assist engineers in developing self-adaptive systems. The manuscript describes the proposed system design, its implementation as part of the Eclipse Arrowhead framework, and its functionality in a smart agriculture use case. The system is designed and implemented in such a way that it can be reused and extended for a variety of use cases without requiring major changes

Web2Touch 2019: Semantic Technologies for Smart Information Sharing and Web Collaboration

This foreword introduces a summary of themes and papers of the Web2Touch (W2T) 2019 Track at the 28th IEEE WETICE Conference held in Capri, June 2019. W2T 2019 includes ten full papers and one short paper. They all address relevant issues in the field of information sharing for collaboration, including, big data analytics, knowledge engineering, linked open data, applications of smart Web technologies, and smart care. The papers are a portfolio of hot issues in research and applications of semantics, smart technologies (e.g., IoT, sensors, devices for tele-monitoring, and smart contents management) with crucial topics, such as big data analysis, knowledge representation, smart enterprise management, among the others. This track shows how cooperative technologies based on knowledge representation, intelligent tools, and enhanced Web engineering can enhance collaborative work through smart service design and delivery, so it contributes to radically change the role of the semantic Web and applications

Pruning wound protection products induce alterations in the wood mycobiome profile of Grapevines

Fungal pathogens involved in grapevine trunk diseases (GTDs) may infect grapevines throughout their lifetime, from nursery to vineyard, via open wounds in stems, canes or roots. In vineyards, pruning wound protection products (PWPPs) offer the best means to reduce the chance of infection by GTD fungi. However, PWPPs may affect non-target microorganisms that comprise the natural endophytic mycobiome residing in treated canes, disrupting microbial homeostasis and indirectly influencing grapevine health. Using DNA metabarcoding, we characterized the endophytic mycobiome of one-year-old canes of cultivars Cabernet Sauvignon and Syrah in two vineyards in Portugal and Italy and assessed the impact of established and novel PWPPs on the fungal communities of treated canes. Our results reveal a large fungal diversity (176 taxa), and we report multiple genera never detected before in grapevine wood (e.g., Symmetrospora and Akenomyces). We found differences in mycobiome beta diversity when comparing vineyards (p = 0.01) but not cultivars (p > 0.05). When examining PWPP-treated canes, we detected cultivar- and vineyard-dependent alterations in both alpha and beta diversity. In addition, numerous fungal taxa were over- or under-represented when compared to control canes. Among them, Epicoccum sp., a beneficial genus with biological control potential, was negatively affected by selected PWPPs. This study demonstrates that PWPPs induce alterations in the fungal communities of grapevines, requiring an urgent evaluation of their direct and indirect effects on plants health with consideration of factors such as climatic conditions and yearly variations, in order to better advise viticulturists and policy makers.info:eu-repo/semantics/publishedVersio

SpaceSheep: comunicações de satélite para cenários de agricultura inteligente

The need to increase productivity in daily activities has been contributing to the development of new systems that can optimize those tasks. Within the agricultural sector, IoT solutions are allowing the autonomous monitoring of crops and animals, reducing human effort and, consequently, the cost of the final product. One of those solutions was developed under the scope of the SheepIT project, which is an animal monitoring system developed to remove weeds in vineyards by controlling the behaviour of herds. To do so, each animal is equiped with a sensorand actuator-based device (collar), which monitors and conditions its actions. The information these devices collect is periodically forwarded to an aggregator node (gateway) through fixed nodes spread around the pasture area (beacon), where is then processed and uploaded to a remote computational platform via the Internet. However, these animals typically move around extensive areas with poor or non-existent ground network coverage, which inhibits the proper communications operation of such system. This work aimed to mitigate the common lack of coverage in rural areas. To do so, a satellite communications interface was integrated into the SheepIT project and, consequently, the messages exchanged by the system were adapted and optimized to meet the constraints of this new technology. These modifications extend the SheepIT project to be able to operate in scenarios where ground network coverage is not available.A necessidade do aumento de produtividade de atividades diárias tem vindo a contribuir para o desenvolvimento de novos sistemas que consigam otimizar essas tarefas. Dentro do sector agrícola, soluções de IoT têm permitido a monitorização autónoma de plantações e animais, reduzindo o esforço humano e, consequentemente, o custo do produto final. Uma dessas soluções foi desenvolvida no âmbito do projeto SheepIT, um sistema de monitorização animal desenvolvido de forma a remover espécies infestantes em vinhas atráves do controlo do comportamento de rebanhos. Para isso, cada animal está equipado com um dispositivo com sensores e atuadores (collar), que monitoriza e condiciona as suas ações. A informação recolhida por estes dispositivos é enviada periodicamente para um nó agregador (gateway) através de nós fixos espalhados pela área de pasto (beacon), onde é então processada e transferida para uma plataforma computacional remota atráves da Internet. Todavia, estes animais deslocam-se tipicamente por extensas áreas com cobertura de rede terrestre fraca ou inexistente, inibindo o correto funcionamento de tal sistema. Este trabalho visou mitigar a ausência de cobertura comum em áreas rurais. Para tal, uma interface de comunicações satélite foi integrada no projeto SheepIT e, consequentemente, as mensagens trocadas pelo sistema foram adaptadas e otimizadas de forma a responder às limitações desta nova tecnologia. Estas modificações extendem o projeto SheepIT para operar em cenários em que a cobertura de rede terrestre não está disponível.Mestrado em Engenharia de Computadores e Telemátic

Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering

Research involving diverse but related data sets, where associations between covariates and outcomes may vary, is prevalent in various fields including agronomic studies. In these scenarios, hierarchical models, also known as multilevel models, are frequently employed to assimilate information from different data sets while accommodating their distinct characteristics. However, their structure extend beyond simple heterogeneity, as variables often form complex networks of causal relationships. Bayesian networks (BNs) provide a powerful framework for modelling such relationships using directed acyclic graphs to illustrate the connections between variables. This study introduces a novel approach that integrates random effects into BN learning. Rooted in linear mixed-effects models, this approach is particularly well-suited for handling hierarchical data. Results from a real-world agronomic trial suggest that employing this approach enhances structural learning, leading to the discovery of new connections and the improvement of improved model specification. Furthermore, we observe a reduction in prediction errors from 28\% to 17\%. By extending the applicability of BNs to complex data set structures, this approach contributes to the effective utilisation of BNs for hierarchical agronomic data. This, in turn, enhances their value as decision-support tools in the field.Comment: 28 pages, 5 figure