Crowd sensing and forecasting for Smart Cities

Dissertação de mestrado integrado em Engenharia InformáticaA utilização de inteligência sob forma de tecnologia no nosso dia-a-dia é uma realidade em crescimento e, portanto, devemos fazer uso da tecnologia disponível para melhorar várias áreas do nosso quotidiano. Por exemplo, a tecnologia atual permite a conceção de sensores inteligentes, mais especificamente sensores de multidão, para detetar passiva mente dispositivos como smartphones ou smartwatches através de probe requests emitidos por estes dispositivos que, por sua vez, fazem parte de um processo de comunicação que ocorre sempre que o Wi-Fi dos dispositivos está ativado. Adicionalmente, crowd sensing - uma solução de Ambient Intelligence (AmI) - é estudada hoje em dia em várias áreas com bons resultados. Portanto, esta dissertação visa investigar e utilizar sensores de multidão para capturar passivamente dados acerca da densidade de multidões, explorar as capacidades do sensor escolhido, analisar e processar os dados para obter melhores estimativas, e conceber e desenvolver modelos de Machine Learning (ML) para prever a densidade nas áreas sensorizadas. Áreas nas quais o sensor de multidão está inserido - AmI, Smart Cities, Wi-Fi Probing - são estudadas, juntamente com a análise de diferentes abordagens ao crowd sensing, assim como paradigmas e algoritmos de ML. Em seguida, é explicado como os dados foram capturados e analisados, seguido por uma experiência feita às capacidades do sensor. Além disso, é apresentado como os modelos de ML foram concebidos e otimizados. Finalmente, os resultados dos vários testes de ML são discutidos e o modelo com melhor desempenho é apresentado. A investigação e os resultados práticos abrem perspetivas importantes para a implementação deste tipo de soluções na nossa vida diária.Bringing intelligence to our everyday environments is a growing reality and therefore we should take advantage of the technology available to improve several areas of our daily life. For example, current technology allows the conception of smart scanners, more specifically crowd sensors, to passively detect devices such as smartphones or smartwatches through probe requests emitted by such devices, that, in turn, are part of a communication process that happens every time the devices’ Wi-Fi is enabled. Additionally, crowd sensing - an Ambient Intelligence (AmI) solution - is being studied nowadays in several areas with good results. Therefore, this dissertation aims to research and use crowd sensors to passively collect crowd density data, explore the capabilities of the chosen sensor, analyse and process the data to get better estimations and conceive and develop Machine Learning (ML) models to forecast the density of the sensed areas. Areas in which crowd sensing is inserted - AmI, Smart Cities, Wi-Fi probing - are studied, along with the analysis of different crowd sensing approaches and ML paradigms and algorithms. Then, it’s explained how the data was collected and analysed together with the insights obtained from it, followed by an experiment done on the crowd sensor capabilities. Moreover, it’s presented how the ML models were conceived and tuned. Finally, the results from the ML several tests are discussed and the best performing model is found. The investigation, together with practical results, opens important perspectives for the implementation of these kinds of solutions in our daily lives

Комп’ютерна система формування рекламних пропозицій у суспільних мережах з використанням соціальних графів та Big Data

Об’єкт дослідження: Комп’ютерна система формування рекламних пропозицій у суспільних мережах з використанням соціальних графів та Big Data. Мета роботи: Розробити та впровадити комп’ютерну систему формування рекламних пропозицій у суспільних мережах з використанням соціальних графів та Big Data, для підвищення ефективності рекламних кампаній через створення теплих аудиторій за допомогою вилучення, аналізу та встановлення відповідностей у великих даних. Одержані результати: було розроблено програмне забезпечення для збору та аналізу даних про пошуки та пропозиції турів. На основі отриманих зв'язків побудований граф, на якому виконаний алгоритм по знаходженню найбільш оптимальної безлічі співпавших пар. За допомогою даної методики було продано 39 турів на різні напрямки за 1,5 тижня за допомогою таргетованої реклами під час пандемії

E-Science as a Catalyst for Transformational Change in University Research Libraries: A Dissertation

Changes in how research is conducted, from the growth of e-science to the emergence of big data, have lead to new opportunities for librarians to become involved in the creation and management of research data, at the same time the duties and responsibilities of university libraries continue to evolve. This study examines those roles related to e-science while exploring the concept of transformational change and leadership issues in bringing about such a change. Using the framework established by Levy and Merry for first- and second-order change, four case studies of libraries whose institutions are members in the Association of Research Libraries (ARL) are developed. The case studies highlight why the libraries became involved in e-science, the role librarians are assuming related to data management education and policy, and the provision of e-science programs and services. Each case study documents the structural and programmatic changes that have occurred in a library to provide e-science services and programs, the future changes library leaders are working to implement, and the change management process used by managerial leaders to bringing about, and permanently embed those changes into the library culture. Themes such as vision, team leadership, the role of library administrators, skills of library staff, and fostering a learning organization are discussed in the context of e-science and leading transformational change. The transformational change included a change in culture, organization paradigm, and redefining the role of the university research library